{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 11 - Model Checking and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "+ Introduce the concept of external validation.\n",
    "+ Introduce the concept of posterior predictive checking.\n",
    "+ Demonstrate posterior predictive checking through numerical examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Readings\n",
    "\n",
    "+ [Ch. 6 of Gelman et al.](https://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/158488388X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## External validation\n",
    "\n",
    "In *external validation* we use the trained model to make predictions about future data.\n",
    "We then collect these future data and compare them to the predictions of the model.\n",
    "The model is good if the predictive probability densities of important quantities of interest match the empirical distributions of the collected data.\n",
    "External validation is the ultimate goal and we should all target it.\n",
    "However, in many cases we either cannot collect any more data or are simply waiting for the new data to arrive.\n",
    "What do we do then?\n",
    "Can you use our existing data to approximate external validation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Posterior Predictive Checking\n",
    "\n",
    "Assume that we have built a model using some data, say $y_{1:n}$.\n",
    "Now, assume that you do the experiment again under the same conditions.\n",
    "What data does your model tell you that you would observe?\n",
    "The posterior predictive distribution of the *replicated data* $y^{\\text{rep}}_{1:n}$ is simply:\n",
    "$$\n",
    "p(y^{\\text{rep}}_{1:n}|y_{1:n}) = \\int p(y^{\\text{rep}}_{1:n}|\\theta)p(\\theta|y_{1:n})d\\theta,\n",
    "$$\n",
    "where $p(y^{\\text{rep}}_{1:n}|\\theta)$ is just the likelihood and $p(\\theta|y_{1:n})$ the posterior.\n",
    "The idea of *posterior predictive checking* is to repeatedly sample $y^{\\text{rep}}_{1:n}$ and compare their characteristics to the true data.\n",
    "You may reject a model that performs very poorly under predictive checking.\n",
    "However, you cannot accept a model that performs very well.\n",
    "There are other methods for doing this which are more involved.\n",
    "Predictive checking is good for identifying bugs in your code or coming up with ideas to extend the models in a way that better matches the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visual Inspections of Replicated Data\n",
    "\n",
    "The idea here is to simply sample $y^{\\text{rep}}_{1:n}$ and compare it visually to $y_{1:n}$.\n",
    "Let's see this on some examples from previous lectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: Inferring the Probability of a Coin Toss\n",
    "\n",
    "Consider $n$ coin-tosses with probability of heads ($1$) $\\theta$, i.e.,\n",
    "$$\n",
    "y_{i}|\\theta \\sim \\operatorname{Ber}(\\theta).\n",
    "$$\n",
    "We put a uniform prior on $\\theta$:\n",
    "$$\n",
    "\\theta\\sim U([0,1]),\n",
    "$$\n",
    "and we have already seen that the posterior is a Beta:\n",
    "$$\n",
    "\\theta|y_{1:n} \\sim \\operatorname{Beta}\\left(1 + \\sum_{i=1}^ny_i, 1 + n - \\sum_{i=1}^ny_i\\right).\n",
    "$$\n",
    "Now, it is actually possible to get $p(y^{\\text{rep}}_{1:n} | y_{1:n})$ analytically in this case.\n",
    "However, we will not bother doing it.\n",
    "We can simply sampling from it as follows:\n",
    "+ First, sample a $\\theta$ from the posterior $p(\\theta|y_{1:n})$.\n",
    "+ Second, sample a $y^{\\text{rep}}$ from the likelihood $p(y^{\\text{rep}}|\\theta)$.\n",
    "+ Repeat steps one and two as many times as needed.\n",
    "\n",
    "Ok, let's start with a dataset that is 100\\% known that it is compatible from the model because we are simply simulating it.\n",
    "Here you go:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1d14b190>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take a fake coin which is a little bit biased - Presumably unknown to us\n",
    "theta_true = 0.5\n",
    "# Here is the underlying random variable from which we will sample\n",
    "Y = st.bernoulli(theta_true)\n",
    "# Sample from it a number of times to generate our y = (y1, ..., xn)\n",
    "n = 50\n",
    "y = Y.rvs(size=n)\n",
    "# Now we are ready to calculate the posterior which the Beta we have above\n",
    "alpha = 1.0 + y.sum()\n",
    "beta = 1.0 + n - y.sum()\n",
    "Theta_post = st.beta(alpha, beta)\n",
    "# Now we can plot the posterior PDF for theta\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "thetas = np.linspace(0, 1, 100)\n",
    "ax.plot([theta_true], [0.0], 'o', markeredgewidth=2, markersize=10, label='True value')\n",
    "ax.plot(thetas, Theta_post.pdf(thetas), label=r'$p(\\theta|y_{1:n})$')\n",
    "ax.set_xlabel(r'$\\theta$')\n",
    "ax.set_title('$n={0:d}$'.format(n))\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have fitted the model, let's draw many replicated data $y^{\\text{rep}}$ from the posterior predictive and compare them to the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw replicated data\n",
    "n_rep = 9\n",
    "y_rep = np.ndarray((n_rep, n))\n",
    "for i in range(n_rep):\n",
    "    theta_post_sample = Theta_post.rvs()\n",
    "    y_rep[i, :] = st.bernoulli(theta_post_sample).rvs(size=n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the data as images.\n",
    "The first row of pixels is are the observed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 4500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(30, 10), dpi=150)\n",
    "ax.imshow(np.vstack([y, y_rep]));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This visual inspection does not reveal anything strange.\n",
    "Let's now repeat this excersize with a dataset that is completely artificial and does not match the model.\n",
    "This a dataset that we are making by hand trying to emulate a coin toss with probability of heads equal to $0.8$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1d599cd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_2 = np.array([0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0])\n",
    "alpha_2 = 1.0 + y_2.sum()\n",
    "beta_2 = 1.0 + n - y_2.sum()\n",
    "Theta_2_post = st.beta(alpha_2, beta_2)\n",
    "# Now we can plot the posterior PDF for theta\n",
    "fig, ax = plt.subplots()\n",
    "thetas = np.linspace(0, 1, 100)\n",
    "ax.plot(thetas, Theta_2_post.pdf(thetas), label=r'$p(\\theta|y_{1:n})$')\n",
    "ax.set_xlabel(r'$\\theta$')\n",
    "ax.set_title('$n={0:d}$'.format(n))\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no true value for $\\theta$ here as I actually picked the data by hand.\n",
    "Let's see if the visual comparison to replicated data reveals that the data did not really come from a fair coin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 4500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw replicated data\n",
    "n_rep = 9\n",
    "y_2_rep = np.ndarray((n_rep, n))\n",
    "for i in range(n_rep):\n",
    "    theta_2_post_sample = Theta_2_post.rvs()\n",
    "    y_2_rep[i, :] = st.bernoulli(theta_2_post_sample).rvs(size=n)\n",
    "fig, ax = plt.subplots(figsize=(30, 10), dpi=150)\n",
    "ax.imshow(np.vstack([y_2, y_2_rep]));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you pay close attention you will notice that the data I picked by hand has more transitions from heads to tails than the replicated data.\n",
    "In other words, the replicated data seem to have longer consecutive series of either heads or tails.\n",
    "How can we see this more clearly?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test quantities and visual inspections\n",
    "\n",
    "We use *test quantities* to characterize the discrepancy between the model and the data.\n",
    "In particular, test quantities help us zoom into the characteristsics of the data that are of particular interest to us.\n",
    "Mathematically, a test quantity is a scalar function of the data and the model parameters $T(y_{1:n},\\theta)$.\n",
    "There are some general recipies for creating test quantities for regression and classification.\n",
    "However, in general, you must use common sense in selecting them.\n",
    "What are the important characteristics of the data that your model should be capturing.\n",
    "We will be seeing specific examples below.\n",
    "\n",
    "Now, assume that you have selected one, or more, test quantities.\n",
    "What do you do with them?\n",
    "Well, the easiest thing to do is a visual comparison of the histogram of the test quantity over replicated data, i.e., of\n",
    "$p(T(y^{\\text{rep}}_{1:n},\\theta) | y_{1:n})$, and compare it to the observe value $T(y_{1:n},\\theta)$.\n",
    "In these plots you are trying to see how likely or unlikely is observed test quantity according to your model.\n",
    "We will be visualizing them next for the coin toss example.\n",
    "\n",
    "### Test quantities and Bayesian $p$-values\n",
    "\n",
    "Antother thing that you can do to check your model is to evaluate the probability that the replicated data give you a test quantity that is more extreme than the observed value.\n",
    "This probability is known as the posterior (or Bayesian) $p$-value and it is defined by:\n",
    "$$\n",
    "p_B = \\mathbb{P}(T(y^{\\text{rep}}_{1:n},\\theta) > T(y_{1:n},\\theta) | y_{1:n}) = \\int 1_{[T(y_{1:n},\\theta),\\infty]}(T(y^{\\text{rep}}_{1:n})) p(y^{\\text{rep}}_{1:n}, \\theta|y_{1:n}) dy^{\\text{rep}}_{1:n}d\\theta.\n",
    "$$\n",
    "Of course, you can just estimate the Bayesian $p$-value using Monte Carlo sampling from the joint posterior of $y^{\\text{rep}}_{1:n}$ and $\\theta$ conditioned on the data $y_{1:n}$.\n",
    "\n",
    "How should I interpret the Bayesian $p$-values?\n",
    "**It is not the probability that your model is correct.**\n",
    "We will derive this probability in the Bayesian model selection lecture.\n",
    "The Bayesian $p$-value is the probability that replications of the experiment will yield test quantitys that exceed the observed value under the assumption that your model is correct.\n",
    "So, here is a nice way to interpret them:\n",
    "+ A Bayesian $p$-value close to $0$ or $1$ indicates that the observed test quantity is unlikely under the assumption that your model is correct. So your model does not capture this aspect of the data. You probably need expand/modify your model somehow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: Inferring the Probability of a Coin Toss - Selecting and Evaluating Test Quantities\n",
    "\n",
    "What are some good test quantities that we can pick for this example.\n",
    "An obvious one is the number of heads.\n",
    "This is only a function of the data.\n",
    "It is:\n",
    "$$\n",
    "T_{h}(y_{1:n}) = \\sum_{i=1}^ny_i.\n",
    "$$\n",
    "Let's implement this as a Python function of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def T_h(y):\n",
    "    \"\"\"\n",
    "    This is an implementation of a \n",
    "    \"\"\"\n",
    "    return y.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, that we have run this example with two datasets: one that was generated from the correct model and one that was generated by hand.\n",
    "We will see the results that we get from both.\n",
    "For the first dataset (the one generated by the model) we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The observed test quantity is 22\n",
      "The Bayesian p_value is 0.4460\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The observed test quantity\n",
    "T_h_obs = T_h(y)\n",
    "print('The observed test quantity is {0:d}'.format(T_h_obs))\n",
    "# Draw replicated data\n",
    "n_rep = 1000\n",
    "y_rep = np.ndarray((n_rep, n))\n",
    "for i in range(n_rep):\n",
    "    theta_post_sample = Theta_post.rvs()\n",
    "    y_rep[i, :] = st.bernoulli(theta_post_sample).rvs(size=n)\n",
    "# Evaluate the test quantity\n",
    "T_h_rep = np.ndarray(y_rep.shape[0])\n",
    "for i in range(y_rep.shape[0]):\n",
    "    T_h_rep[i] = T_h(y_rep[i, :])\n",
    "# Estimate the Bayesian p-value\n",
    "p_val = np.sum(np.ones((n_rep,))[T_h_rep > T_h_obs]) / n_rep\n",
    "print('The Bayesian p_value is {0:1.4f}'.format(p_val))\n",
    "# Do the plot\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "tmp = ax.hist(T_h_rep, density=True, alpha=0.25, label='Replicated test quantity')[0]\n",
    "ax.plot(T_h_obs * np.ones((50,)), np.linspace(0, tmp.max(), 50), 'k', label='Observed test quantity')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's look also at the other dataset (the one we picked by hand):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The observed test quantity is 22\n",
      "The Bayesian p_value is 0.4890\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The observed test quantity\n",
    "T_h_2_obs = T_h(y_2)\n",
    "print('The observed test quantity is {0:d}'.format(T_h_2_obs))\n",
    "# Draw replicated data\n",
    "n_rep = 1000\n",
    "y_2_rep = np.ndarray((n_rep, n))\n",
    "for i in range(n_rep):\n",
    "    theta_2_post_sample = Theta_2_post.rvs()\n",
    "    y_2_rep[i, :] = st.bernoulli(theta_2_post_sample).rvs(size=n)\n",
    "# Evaluate the test quantity\n",
    "T_h_2_rep = np.ndarray(y_2_rep.shape[0])\n",
    "for i in range(y_rep.shape[0]):\n",
    "    T_h_2_rep[i] = T_h(y_2_rep[i, :])\n",
    "# Estimate the Bayesian p-value\n",
    "p_val_2 = np.sum(np.ones((n_rep,))[T_h_2_rep > T_h_2_obs]) / n_rep\n",
    "print('The Bayesian p_value is {0:1.4f}'.format(p_val_2))\n",
    "# Do the plot\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "tmp = ax.hist(T_h_2_rep, density=True, alpha=0.25, label='Replicated test quantity')[0]\n",
    "ax.plot(T_h_2_obs * np.ones((50,)), np.linspace(0, tmp.max(), 50), 'k', label='Observed test quantity')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks about the same. This just means that I was able to replicate this particular test quantity when I picked values by hand.\n",
    "Can we find a better statistic?\n",
    "Remember our observation when we plotted the replicated data vs the true data for the second case.\n",
    "We observed that my hand-picked data included more transitions from heads to tails.\n",
    "Let's build a statistic that captures that.\n",
    "We are going to take this:\n",
    "$$\n",
    "T_s(y) = \\text{# number of switches from 0 and 1 in the sequence}\\;y.\n",
    "$$\n",
    "This is not easy to write in an analytical form, but we can program it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def T_s(y):\n",
    "    s = 0\n",
    "    for i in range(1, y.shape[0]):\n",
    "        if y[i] != y[i-1]:\n",
    "            s += 1\n",
    "    return s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look first at the original dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The observed test quantity is 24\n",
      "The Bayesian p_value is 0.4340\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The observed test quantity\n",
    "T_s_obs = T_s(y)\n",
    "print('The observed test quantity is {0:d}'.format(T_s_obs))\n",
    "# Draw replicated data\n",
    "n_rep = 1000\n",
    "y_rep = np.ndarray((n_rep, n))\n",
    "for i in range(n_rep):\n",
    "    theta_post_sample = Theta_post.rvs()\n",
    "    y_rep[i, :] = st.bernoulli(theta_post_sample).rvs(size=n)\n",
    "# Evaluate the test quantity\n",
    "T_s_rep = np.ndarray(y_rep.shape[0])\n",
    "for i in range(y_rep.shape[0]):\n",
    "    T_s_rep[i] = T_s(y_rep[i, :])\n",
    "# Estimate the Bayesian p-value\n",
    "p_val = np.sum(np.ones((n_rep,))[T_s_rep > T_s_obs]) / n_rep\n",
    "print('The Bayesian p_value is {0:1.4f}'.format(p_val))\n",
    "# Do the plot\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "tmp = ax.hist(T_h_rep, density=True, alpha=0.25, label='Replicated statistic')[0]\n",
    "ax.plot(T_s_obs * np.ones((50,)), np.linspace(0, tmp.max(), 50), 'k', label='Observed test quantity')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks ok. \n",
    "\n",
    "Let's now look at the one I picked by hand:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The observed test quantity is 36\n",
      "The Bayesian p_value is 0.0010\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The observed test quantity\n",
    "T_s_2_obs = T_s(y_2)\n",
    "print('The observed test quantity is {0:d}'.format(T_s_2_obs))\n",
    "# Draw replicated data\n",
    "n_rep = 1000\n",
    "y_2_rep = np.ndarray((n_rep, n))\n",
    "for i in range(n_rep):\n",
    "    theta_2_post_sample = Theta_2_post.rvs()\n",
    "    y_2_rep[i, :] = st.bernoulli(theta_2_post_sample).rvs(size=n)\n",
    "# Evaluate the test quantity\n",
    "T_s_2_rep = np.ndarray(y_2_rep.shape[0])\n",
    "for i in range(y_rep.shape[0]):\n",
    "    T_s_2_rep[i] = T_s(y_2_rep[i, :])\n",
    "# Estimate the Bayesian p-value\n",
    "p_val_2 = np.sum(np.ones((n_rep,))[T_s_2_rep > T_s_2_obs]) / n_rep\n",
    "print('The Bayesian p_value is {0:1.4f}'.format(p_val_2))\n",
    "# Do the plot\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "tmp = ax.hist(T_h_2_rep, density=True, alpha=0.25, label='Replicated statistic')[0]\n",
    "ax.plot(T_s_2_obs * np.ones((50,)), np.linspace(0, tmp.max(), 50), 'k', label='Observed test quantity')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data are highly unlikely under the assumptions of this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Measuring the Acceleration of Gravity\n",
    "\n",
    "Assume that we are performing an experiment $y_i$ that measures the acceleration of gravity and that we know that the measurement variance is $\\sigma = 0.1$.\n",
    "So, we have:\n",
    "$$\n",
    "y_i | g, \\sigma \\sim N(g, \\sigma^2).\n",
    "$$\n",
    "So, the model says that the measured acceleration of gravity is around the true one with some Gaussian noise.\n",
    "Assume that it is adequately captured by:\n",
    "$$\n",
    "g | g_0, s_0 \\sim N(g_0, s_0^2),\n",
    "$$\n",
    "with known $g_0 = 10$, $s_0 = 0.4$.\n",
    "We saw in lecture 6 that our state of knowledge about the paramter $g$ is captured by:\n",
    "$$\n",
    "g|y_{1:n}, \\sigma, g_0, s_0 \\sim N\\left(\\frac{1}{\\frac{n}{\\sigma^2} + \\frac{1}{s_0^2}}\\left(\\frac{g_0}{s_0^2} + \\frac{\\sum_{n=1}^Ny_n}{\\sigma^2}\\right),\n",
    "\\left(\\frac{1}{s_0^2} + \\frac{N}{\\sigma^2}\\right)^{-1}\n",
    "\\right).\n",
    "$$\n",
    "Now, let's put this model to the test using data that have different measurement noise than the one that our model assumes.\n",
    "In particular, let's assume that our measurement device rounds numbers to the first digit and that it is capped, for some reason, at $10.1 m/s^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$y_i$')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the true acceleration of gravity from scipy\n",
    "import scipy.constants\n",
    "np.random.seed(12345)\n",
    "g_true = scipy.constants.g\n",
    "# Generate some synthetic data\n",
    "n = 50\n",
    "# The true noise variance\n",
    "sigma = 0.4\n",
    "y = np.round(g_true + sigma * np.random.randn(n), 1)\n",
    "y[y > 10.1] = 10.1\n",
    "fig, ax = plt.subplots(dpi=100)\n",
    "ax.plot(y, 'o', label='Data')\n",
    "ax.set_xlabel('$i$')\n",
    "ax.set_ylabel('$y_i$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now visualize the posterior that we get with our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The posterior of g is N(9.73, (0.05)^2)\n"
     ]
    }
   ],
   "source": [
    "# Our prior state of knowledge\n",
    "g0 = 9.8\n",
    "s0 = 0.2\n",
    "G_prior = st.norm(g0, s0)\n",
    "# We shoed that the posterior for g conditioned on data is a Gaussian with mean\n",
    "gn = 1.0 / (n / sigma ** 2 + 1.0 / s0 ** 2) * (g0 / s0 ** 2 + y.sum() / sigma ** 2)\n",
    "# And standard deviation\n",
    "sigman = np.sqrt(1.0 / (1.0 / s0 ** 2 + n / sigma ** 2))\n",
    "print('The posterior of g is N({0:1.2f}, ({1:1.2f})^2)'.format(gn, sigman))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here is a visualization of our posterior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's draw this\n",
    "G_post = st.norm(gn, sigman)\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "ggs = np.linspace(g0 - 6.0 * s0, g0 + 6.0 * s0, 100)\n",
    "ax.plot(ggs, G_prior.pdf(ggs), label='prior')\n",
    "ax.plot(y, np.zeros(y.shape), 'x', label='data')\n",
    "ax.plot(ggs, G_post.pdf(ggs), label='posterior')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the replicated data versus the true data and see if we can spot a problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_rep = 9\n",
    "y_rep = np.ndarray((n_rep, n))\n",
    "for i in range(n_rep):\n",
    "    g_post_sample = G_post.rvs()\n",
    "    y_rep[i, :] = g_post_sample + sigma * np.random.randn(n)\n",
    "fig, ax = plt.subplots(5, 2, sharex='all', sharey='all', figsize=(20, 20))\n",
    "ax[0, 0].plot(np.arange(n), y, 'ro');\n",
    "for i in range(1, n_rep + 1):\n",
    "    ax[int(i / 2), i % 2].plot(np.arange(n), y_rep[i-1], 'bo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, here you can clearly see the difference between the replicated data and the observations.\n",
    "Is there a good test quantity to summarize this difference?\n",
    "Let's look at this:\n",
    "$$\n",
    "T(y) = \\text{# of observations greater than 10.2}\\;m/s^2.\n",
    "$$\n",
    "Here is how we can program it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def T_g_1(y):\n",
    "    return int(np.ones(y.shape)[y > 10.2].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The observed test quantity is 0\n",
      "The Bayesian p_value is 0.9927\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The observed test quantity\n",
    "T_g_obs = T_g_1(y)\n",
    "print('The observed test quantity is {0:d}'.format(T_g_obs))\n",
    "# Draw replicated data\n",
    "n_rep = 3000\n",
    "y_rep = np.ndarray((n_rep, n))\n",
    "for i in range(n_rep):\n",
    "    g_post_sample = G_post.rvs()\n",
    "    y_rep[i, :] = g_post_sample + sigma * np.random.randn(n)\n",
    "# Evaluate the test quantity\n",
    "T_g_rep = np.ndarray(y_rep.shape[0])\n",
    "for i in range(y_rep.shape[0]):\n",
    "    T_g_rep[i] = T_g_1(y_rep[i, :])\n",
    "# Estimate the Bayesian p-value\n",
    "p_val = np.sum(np.ones((n_rep,))[T_g_rep > T_g_obs]) / n_rep\n",
    "print('The Bayesian p_value is {0:1.4f}'.format(p_val))\n",
    "# Do the plot\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "tmp = ax.hist(T_g_rep, density=True, alpha=0.25, label='Replicated test quantity')[0]\n",
    "ax.plot(T_g_obs * np.ones((50,)), np.linspace(0, tmp.max(), 50), 'k', label='Observed test quantity')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, you clearly see what is wrong with the model. Too many replicated data points are expected to exceed $10\\;m/s^2$ than the observed value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Number of Earthquakes in San Andreas Fault\n",
    "\n",
    "This is the model we built in Homework 1, Problem 6.\n",
    "We repeat the description here.\n",
    "\n",
    "The [San Andreas fault](https://en.wikipedia.org/wiki/San_Andreas_Fault) extends through California forming the boundary between the Pacific and the North American tectonic plates.\n",
    "It has caused some of the major earthquakes on Earth.\n",
    "We are going to focus on Southern California and we would like to assess the probability of a major earthquake, defined as an earthquake of magnitude 6.5 or greater, during the next ten years.\n",
    "\n",
    "The first thing we are going to do is go over a [database of past earthquakes](https://scedc.caltech.edu/significant/chron-index.html) that have occured in Southern California and collect the relevant data. We are going to start at 1900 because data before that time may are unreliable.\n",
    "Go over each decade and count the occurence of a major earthquake (i.e., count the number of organge and red colors in each decade)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAKoCAYAAAC/aIpIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde5yOdeL/8fc1M8YwzjORcSoUWoclxxxmSYpSJBWl3Vpth7W7rVSETqxD+KVW28ESIhISigqDVSnSkvPIMYMYM+bAHO/P74/5zrUmg5l77s8c7nk9Hw+Prvu+r+tzvXtUj0fvx+e6Ph/HGGMEAAAAAH4koKgDAAAAAICvUXQAAAAA+B2KDgAAAAC/Q9EBAAAA4HcoOgAAAAD8DkUHAAAAgN+h6AAAAADwOxQdAAAAAH6HogMAAADA71B0AAAAAPgdig4AAAAAv0PRAQAAAOB3KDoAAAAA/A5FBwAAAIDfKfZF58SJE0UdAQAAAEAJU2yLzvr16/XEE0+oe/fuRR0FAAAAQAkTVNQBLhQbG6tFixZp4cKFiomJkTFGjuMUdSwAAAAAJUyxKDrffvutFixYoNWrVysjI0PGmKKOBAAAAKAEK7Kik5CQoCVLlujDDz/UoUOHJMktOI7juMdhYWFFFREAAABACVXoRee///2vFixYoFWrVik1NTVHuckuOAEBAercubPuuecede3atbAjAgAAACjhCqXonDt3TsuWLdOCBQu0d+9eSRfP3hhjVK9ePfXr1099+vRR9erVCyMaAAAAAD9ktejs2bNH8+fP14oVK3Tu3LlcZ28CAwN1xx13qF+/fmrTpo3NOAAAAABKCcf4+M3/tLQ0ffrpp1qwYIG2b98uSTlWT/v1TE54eLg2btzoywgAAAAASjmfzegcPHhQCxYs0NKlS5WQkCDp4oITERGhO++8U3fffbd69OjB0tEAAAAArChQ0cnIyNAXX3yhBQsWaPPmzZL+V26yZ25CQkLUvXt33X333Wrfvj3lBgAAAIB1Xhed1157TYsXL1ZsbKwkXbT3TevWrdWnTx/17NlToaGhBUsJAAAAAPngddF55513cszcSFLDhg3Vq1cv9e7dW3Xq1PFJQAAAAADIrwK/oxMQEKDbbrtNTz75pBo2bOiLTAAAAABQIAUuOsYYrVy5Uhs3blTr1q3VvXt3devWTVWqVPFFPgAAAADIN6+LTteuXfWf//xHGRkZkqSEhARFRUUpKipKgYGB6tChg/r27avu3bsrODjYZ4EBAAAA4EoKtI9ObGysPv74Y3388cf66aefcg78f6urVahQQT179lSfPn3UqlUr9/fGjRvLcRyFhYWxjw4AAAAAn/LZhqHbtm3TokWLtHLlSiUlJWUN/n+LFWSXnrp166pPnz6666671K1bN4oOAAAAACt8VnSypaSkaNWqVVq8eLG2bNly0bLTjuO4BcgYo/DwcIoOAAAAAJ/yedG50NGjR7V48WJ98sknOn78eNYNL5jlMcYoMDBQnTt3Vp8+fdStWzfe5wEAAABQYFaLTjZjjL766istXrxYa9asUVpaWtbN/++RtmwVK1bUbbfdpjvvvFOtW7e2HQsAAACAnyqUonOhhIQELVu2TEuWLNGuXbuyQlyw8Wh2+alVq5buuusu9e7dW9dcc01hRgQAAABQwhV60bnQnj17tGjRIq1YsULx8fFZgX5VehzHcQsRAAAAAORFkRadbOnp6Vq9erWWLFmir7/+WpmZmTne5dm9e3dRRwQAAABQghSLonOhkydPunvzHD58mKIDAAAAIN+KXdG50ObNm7VkyRKNHz++qKMAAAAAKEGKddEBAAAAAG8E+HrA1NRUHT16NM/nv/baa5o7d67OnDnj6ygAAAAASimfzegkJyfrrbfe0ocffqiOHTtq6tSpV7zG4/Gobdu2Sk5OVtmyZTVgwAA9+eSTqlixoi8iAQAAACilfDKj8+OPP6pXr16aMWOGEhMTtWXLljxdt2PHDiUlJUmSUlJSNGvWLPXt21c7d+70RSwAAAAApVSBi87333+vQYMG6eTJk+53sbGxOnLkyBWvPXTokIKCgtxlpCXp559/1kMPPcTeOQAAAAC8VqBH106dOqU777xTcXFx7r43tWvX1oMPPqj+/fsrNDT0imMkJyfrs88+08yZM3Xw4EF3nKuuukqffvqpKlWq5G08AAAAAKVUgWZ0Xn31VbfkOI6jIUOGaOXKlfrDH/6Qp5IjSaGhoerfv7+WLVumP/zhD+7szunTpzV58uSCxAMAAABQSnk9oxMTE6Pu3bsr+/J//OMfuvvuuwsc6LXXXtM777wjSSpTpozWrVunsLCwAo8LAAAAoPTwekZn+fLl8ng8kqSePXv6pORI0lNPPaUWLVpIkjIyMrR8+XKfjAsAAACg9PC66GzdutU9fvTRR30SRpIcx9Fjjz3mft68ebPPxgYAAABQOnhddKKjoyVJ1apVU5MmTXwWSJI6d+6skJAQSdLevXt9OjYAAAAA/+d10YmPj5fjOKpbt64v80jKejenfv36MsYoLi7O5+MDAAAA8G9eF520tDRJcmdefK1y5cqSpNTUVCvjAwAAAPBfXhedChUqSJISExN9FuZC58+flySVL1/eyvgAAAAA/JfXRSciIkLGGB06dMiHcf7nwIEDchxHVatWtTI+AAAAAP/lddHJXoAgOTlZ27Zt81kgSdq+fbsSEhIkSQ0aNPDp2AAAAAD8n9dFp0uXLu7x/PnzfRIm27x589zj3/72tz4dGwAAAID/87roREZGqnLlyjLGaNmyZdqyZYtPAv3www9asWKF+7lHjx4+GRcAAABA6eF10QkJCdGAAQMkSR6PR3/961+1f//+AoU5ePCg/vrXvyozM1OO46hLly665pprCjQmAAAAgNLH66IjSY899phq1qwpx3F05swZDRgwQEuWLMn3OMYYLV68WPfdd59Onz4tSSpbtqyeeeaZgsQDAAAAUEo5xhhTkAG+//57Pfzww0pPT5cxxt1E9I477lC7du3029/+VsHBwRddl5aWps2bN+urr77Sl19+qZ9//tm9XpLGjBmje+65pyDRAAAAAJRSBS46krRmzRoNGzZMKSkpyh4uu7AEBQUpLCxM1apVU0hIiOLj4xUXF6eEhAR5PB5JcguOMUaBgYEaNWqU+1gcAAAAAOSXT4qOJO3cuVPDhw9XdHS0W3IuHDq377K/z/6ufv36GjduHCutAQAAACgQnxUdScrIyNDChQs1Z86cXDcSvbDUXKhly5a6//771bt3bwUEFOi1IQAAAADwbdG50J49e7Rx40bt2bNHP//8sxITE5Wenq6QkBCFhYXpmmuuUbNmzdShQwfVrFnTRgQAAAAApZS1ogMAAAAARYXnxAAAAAD4HYoOAAAAAL9D0QEAAADgdyg6AAAAAPwORQcAAACA36HoAAAAAPA7FB0AAAAAfoeiAwAAAMDvUHQAAAAA+B2KDgAAAAC/Q9EBAAAA4HcoOgAAAAD8DkUHAAAAgN8JKoybZGRk6OjRo0pMTFRaWpqMMfm6vk2bNpaSAQAAAPBHVovOgQMHNG3aNK1bt07nz5/3agzHcbRr1y4fJwMAAADgz6wVna+//lpPPvmkUlNTc8zgOI6T5zHyO/MDAAAAAJKlopOUlKRhw4YpJSXlomJDeQEAAABgm5Wi8+GHH+rMmTNyHEfGGN1+++0aMGCAGjZsqEqVKikggDUQAAAAANhjpeisXbvWPX744Yf13HPP2bgNAAAAAOTKytTKwYMHJUnlypXTkCFDbNwCAAAAAC7JStFJTEyU4zhq2LChQkNDbdwCAAAAAC7JStEJDw+XJJUtW9bG8AAAAABwWVaKToMGDWSMcR9hAwAAAIDCZKXo3HrrrZKk2NhYbdy40cYtAAAAAOCSHGNhY5u0tDT17dtXP/30k+rVq6ePPvpIlSpV8vVtAAAAACBXVmZ0goODNWXKFFWtWlVHjhxRv379tGLFCp07d87G7QAAAAAgByszOvPmzZMkHTp0SO+//74cx5EkBQQEqHbt2qpSpUqeFypwHEezZ8/2dUQAAAAAfsxK0WncuLFbbrJl3+bX31+OMUaO42j37t0+zQcAAADAvwXZGvhS/clCrwIAAACAHKwUnSFDhtgYFgAAAADyxMqjawAAAABQlKysurZz507eqwEAAABQZKwUnZkzZ+ruu+9W79699dlnn9m4BQAAAABckpWis23bNhljtH//fgUEWLkFAAAAAFySlRZy+vRp97hdu3Y2bgEAAAAAl2Sl6JQvX/5/N2BGBwAAAEAhs9JCunTp4h6vXLnSxi0AAAAA4JKsFJ2nnnpKV199tYwxmjhxolavXm3jNgAAAACQK2v76Pzyyy8aNmyYvvvuOzmOo4YNG6pt27Zq3LixqlWrpgoVKuT5sbY2bdrYiAgAAADAT1kpOh06dJAkGWMUHx//v5s5Tr7HchxHu3bt8lk2AAAAAP4vyMagcXFxbqnxptxkszTZBAAAAMDPWSk6EiUFAAAAQNGx9o4OAAAAABQVNrkBAAAA4HesPboG79x0002KjY3N8d2FG7ACAAAAJdG5c+fcY8dxtGfPHqv3K7Kik5aWJo/Ho5CQkKKKUCydP3/+ou8u/JcCAAAAKOkK4+2ZQik6xhh9/vnnWr9+vbZu3aqTJ08qNTVVnTt31rvvvitJioqK0sKFC/XAAw+oU6dOhRELAAAAgJ+yXnS+/vprvfDCCzp27Jj7XXaDu7DJHT58WFFRUVq3bp0iIyM1ceJEVa5c2Xa8YqdcuXIXzeDw6BoAAABKusJ+Sslq0Zk9e7ZeffVVeTyei6anfr2/zs8//ywpq/ysX79egwYN0rx581SxYkWbEYudr7/+Wi1btnT/RShfvrx++OGHIk4FAAAAFMyv/x/XNmurrn355ZeaMGGCW3JCQkJ0yy236Kmnnsr1/MaNG6tq1aqSsspOdHS0XnrpJVvxAAAAAPgxK0Xn/Pnzevnll91ZnK5du2rt2rX65z//qccffzzXa+655x59+eWX6t27t6SssvPZZ59p9+7dNiICAAAA8GNWis7HH3+s06dPy3EctW7dWtOmTVO1atWueF2FChU0adIkdevWLcdYAAAAAJAfVorOunXr3OORI0cqMDAwX9c//fTT7js8W7du9WU0AAAAAKWAlaITHR0tSapdu7YaN26c7+sbNGigWrVqyRijo0eP+joeAAAAAD9npejExsbKcRxFRER4PUb2tcnJyb6KBQAAAKCUsFJ0QkJCJEmpqalej5GUlCRJCg0N9UmmvEpPT1e/fv3UqFEjNWrUqFDvDQAAAMA3rBSd8PBwGWO0d+9epaWl5fv6hIQE7du3T47j6KqrrrKQ8NJef/117dixo1DvCQAAAMC3rBSd1q1bS5JSUlK0ZMmSfF8/c+ZMZWRkSJJatWrl02yXs2nTJs2YMaPQ7gcAAADADitF59Zbb3WPJ02apD179uT52tWrV2v69Onu55tvvtmn2S4lLi5Ozz77rDweT6HcDwAAAIA9VopOx44d1bp1axljlJycrAcffFDz5s3T+fPnL3nN6dOnNWHCBP3tb39TZmamHMdRkyZNFBkZaSPiRUaNGqWTJ0+qTJkyhXI/AAAAAPZYKTqSNH78eFWrVk2O4ygpKUljx45Vhw4d1L9/f/ecgwcPasSIEerfv78iIyM1e/ZsZWZmSpLKlSuncePG2YqXw/z587V69WpJ0t///vdCuScAAAAAe6wVnTp16uidd95R9erV3e9SUlK0Y8cOdzPQY8eOaenSpdqxY4dbcCSpYsWKmjp1qld78OTXTz/9pAkTJkiS2rdvr0ceecT6PQEAAADYZa3oSFKzZs20dOlS9evXz30kzBgjY0yO4+zPkvS73/1OixcvVpcuXWxGkySlpaVp6NChSklJUZUqVfTqq6+6JQwAAABAyRVk+wZVq1bVP/7xD/39739XVFSUtm7dqiNHjighIUHp6emqXLmyatSooZYtWyoyMlL169e3Hcl14UIJr7zyimrUqFFo9wYAAABgj/Wiky08PFz9+/fP8Y5OUdqwYYPef/99SdLdd9+dY6U4AAAAACWb1UfXiqvY2FiNGDFCxhjVrVtXo0aNKupIAAAAAHyo1BUdY4yGDx+u06dPKygoSJMnT1ZoaGhRxwIAAADgQ4X26Nrx48d1+vRppaam5lh8IC/atGnjsxyzZ8/Whg0bJElPPvmkWrRo4bOxAQAAABQPVovOiRMnNGXKFEVFRSk5OdmrMRzH0a5du3ySZ8+ePZoyZYokqVWrVnr88cd9Mi4AAACA4sVa0dm6dasee+wxJSUl5ZjByc/yzfmd+bmclJQUPf3000pLS1OFChU0adIkBQYG+mx8AAAAX+g4Ya0SU9KLOka+VAwpo6+GdyvqGEAOVopOYmKihg4dqsTExIuKjS/LS3689dZb2r9/vySpadOm+vzzz/N03YwZM9zjLl266LrrrrOSDwAAQJISU9KVkJJR1DGAEs9K0Vm2bJlOnDghx3FkjNFtt92mAQMGqEmTJqpYsWKRbMp58uRJ93jTpk3atGlTnq579dVX3eOqVatSdAAAAIASwErRWbVqlXs8ePBgDRs2zMZtAAAAACBXVorOkSNHJEnlypXTX/7yFxu3yLcJEyZowoQJeTq3UaNG7vHevXttRQIAAABgiZV9dM6cOSPHcdSoUSOVLVvWxi0AAAAA4JKsFJ3KlStnDR5Q6vYjBQAAAFAMWGkiDRo0kDFGhw4dsjE8AAAAAFyWlaLTvXt3SVmPsK1bt87GLQAAAADgkqwUnf79++vqq6+WMUbjxo1TYmKijdsAAAAAQK6sFJ2QkBBNnDhRwcHBOnr0qAYOHKjNmzfbuJUVe/fudf8AAAAAKHm8Xl56xIgRVzynZs2aOnz4sKKjo/XQQw+pSpUqaty4sSpXrqxy5crl6T6O42jcuHHexgQAAABQCnlddD7++GM5jnPF87LPMcYoLi5OmzZtyve9KDoAAAAA8qNAG4YaY6xfk5cyBQAAAAAX8rro9O3b15c5AAAAAMBnvC4648eP92UOAAAAAPAZK6uuAQAAAEBRKtA7OpcSExMjKWuZ6WrVqnk1xv79+7Vjxw4lJyfrgQce8GU8AAAAAH7OStHp1q2bHMdR586d9e6773o1xsiRI7V9+3ZVr16dogMAAAAgX6w+uubNqmzZypUrJ2OMzpw548NEAAAAAEqDYvmOzrFjx7Rr1y5JUnBwcBGnAQAAAFDSeP3o2owZM7R+/frLnrN9+3Y99NBD+Ro3LS1N+/fvV1JSkhzHUUREhLcRAQAAAJRSXhedXr16adq0aUpJScn1d2OMEhIStHnz5nyPbYxxNwrt1auXtxEBAAAAlFJeP7pWs2ZN/fnPf5Yx5qI/2XL7LS9/sq+NjIzUo48+WvC/SwAAAAClSoFWXXv44YeVkZGhjIyMHN9PmzZNjuOobt266t27d57HcxxHZcuWVZUqVXT99derefPmBYkHAAAAoJQqUNEJDAzU448/ftH306ZNkyTVrVtXQ4YMKcgtAAAAACDfrOyjk72AQHh4uI3hAQAAAOCyrBSdtWvX2hgWAAAAAPKkWO6jAwAAAAAFQdEBAAAA4HesPLqW301CL8dxHM2ePdtn4wEAAADwf1aKznfffedu+FkQF24cCgAAAAB5ZaXoSMqxcWheZZcab64FAAAAgGxWik5+9s5JTU1VXFycoqOj9eOPP8rj8SgoKEijRo1S27ZtbcQDAAAA4OeKvOhc6PDhwxo9erS+++47jRs3Tu+++67at2/v43QAAAAA/F2xWnWtXr16+ve//61mzZopLS1Nw4cPV1JSUlHHAgAAAFDCFKuiI0nBwcEaPny4JOnkyZNasmRJEScCAAAAUNIUu6IjSTfeeKNq1qwpSVq1alURpwEAAABQ0hTLoiNJtWvXljFGR44cKeooAAAAAEqYYlt04uPjJUkJCQlFnAQAAABASVMsi87PP/+sgwcPynEchYWFFXUcAAAAACVMsSs6qampGjVqlDIyMiRJzZo1K+JEAAAAAEoaK/vobN68Oc/nGmOUkZGhpKQk7d+/X4sXL1ZMTIz7e69evWxEBAAAAODHrBSdQYMGyXEcr641xshxHDmOo+bNm+u2227zcToAAAAA/q7YPLpmjJExxj1u2LChpk2bVsSpAAAAAJREVmZ0JLmlJa+Cg4NVpUoVNWnSRLfccov69OmjoCBr8QAAAAD4MStNYs+ePTaGBQAAAIA8KTaPrgEAAACAr1B0AAAAAPidQn8JJikpSadPn1Z8fLwCAgIUGhqq2rVrq2zZsoUdBQAAAICfKpSiExcXp/fff19r167Vvn37cl2o4Nprr1VkZKQGDhyoOnXqFEYsAAAAAH7KetFZsGCBJk6cqJSUFEmXXo3t4MGDOnjwoObOnasnnnhCTz75pO1oAAAAAPyU1aIzceJEzZo1y90E9HJLTmefk56ern/+85+KiYnR2LFjbcYDAAAA4KesFZ3Fixfrvffek+M4kqTAwEB169ZNHTt21HXXXafKlSsrIyNDCQkJ2rdvn7766iutX79eHo9HxhgtXrxYzZs317333msrIgAAAAA/ZaXoJCcna9KkSe7nG264QVOmTNG1116b6/lt2rTRAw88oAMHDmjYsGHatWuXjDGaMmWK7rjjDpUvX95GTAAAAAB+ysry0h9//LHi4+PlOI6uvfZazZ49+5Il50L169fXnDlz1LBhQ0lSQkKCli1bZiMiAAAAAD9mpej85z//cY9Hjx6tihUr5vnaChUqaOTIke7nqKgon2YDAAAA4P+sFJ19+/ZJksLDw9WhQ4d8X9+hQwdVr15dxhjt3r3b1/EAAAAA+DkrRefMmTNyHEfXXHON12PUq1dPUtYePAAAAACQH1aKTmBgoCQpPT3d6zGyrw0ODvZJJgAAAAClh5WiEx4eLmOM9u/f71XZSU9PV3R0tBzHUVhYmIWEAAAAAPyZlaLTokULSVnLTC9fvjzf1y9btkzJycmSpObNm/s0GwAAAAD/Z6XodO/eXZJkjNGrr76qgwcP5vnan376Sa+++qr7uVu3bj7PBwAAAMC/WSk6t9xyi+rVqyfHcRQfH6/7779fy5Ytk8fjueQ1mZmZWrp0qQYOHKizZ8/KcRzVrVtXt912m42IAAAAAPxYkI1BAwICNH78eA0aNEgej0dnz57Vc889p0mTJqldu3Zq2LChKlWqJClrU9Do6Gh9++23io2NlTFGUtaCBmPGjFFAgJUuBgAAAMCPWSk6ktSqVStNnjxZzz77rDIyMmSM0alTp/Tpp5/mer4xRo7jZIUKCtLYsWPVtm1bW/EAAAAA+DGr0yU9e/bUnDlz1LRpU/c7Y4w7a/Prz8YYNWnSRPPmzVOfPn1sRgMAAADgx6zN6GRr2bKlPvroI33zzTfasGGDvv/+e/3yyy+Kj4+XMUaVKlVSzZo11apVK0VGRqpDhw62IwEAAADwc9aLTrYOHTpQYgAAAAAUCt70BwAAAOB3KDoAAAAA/E6hPbr2a1u2bNHq1at16NAheTwe1alTR126dFFkZGRRRQIAAADgJ3xWdKKjo7Vw4UJt3bpVzZo100svvZTreXFxcXr22We1cePGi3774IMP1KhRI40bN0433HCDr6IBAAAAKGUKXHQ8Ho8mTZqkOXPmyOPxSJIqVKiQ67kJCQl66KGHtH///hz75ly43PSePXs0YMAAvfnmm+rUqVNB4wEAAAAohQr8js5zzz2nWbNmKTMzU1JWaYmPj8/13FdeeUXR0dGSJMdxZIxR9erV1a1bN7Vu3VpBQVm9KzU1VX/729909OjRgsYDAAAAUAoVqOh8/PHHWr58+f8GCwhQx44d1bdv34vO3b59u1asWOEWHEn605/+pDVr1uhf//qX5s6dq5UrV7qbi547d04TJ04sSDwAAAAApZTXj65lZGTojTfecD/fcMMNmjx5surXr5/r+XPnznWPHcdR9+7dNXTo0Bzn1KlTRzNmzNDtt9+u06dPa82aNTp8+LDq1avnbUwAAAAApZDXMzpbtmzR8ePH5TiOateurTlz5lyy5KSnp2vNmjU5ZnP+8pe/5Hpu5cqV9eijj7qfP/30U28jAgAAACilvC46F66a9tRTT11yAQIpqxQlJye7n5s0aaLrr7/+kuf37Nkzx7UAAAAAkB9eF53vv/9ekhQUFKRbbrnlsudu2rRJktyV1jp37nzZ86tXr65atWrJGKMDBw54GxEAAABAKeV10Tl9+rQk6dprr1VwcPBlz/3uu+9yfG7fvv0Vxw8PD5eUte8OAAAAAOSH10XnzJkzchxHVapUuex5aWlp2rFjh7tnTlBQkFq1anXF8UNCQiRlLXoAAAAAAPnhddHJLiABAZcfYvv27UpPT5eUtdpa06ZNVbZs2SuOf+bMGUnK07kAAAAAcCGvi05YWJgkKSEh4bLnZT+2lr3aWtu2ba84tjFGJ0+elOM4qlq1qrcRAQAAAJRSXhed8PBwGWN0+PDhy563YcOGHJ/z8n7Otm3b3AJVq1YtbyMCAAAAKKW8Ljo33nijJOncuXPuCmy/dvz4cW3bts19Pyc0NFRt2rS54tgX7p3zm9/8xtuIAAAAAEopr4tOly5d3ONp06bles6cOXPcR9Ycx1H37t0VFBR02XEPHz6sBQsWuJ87duzobUQAAAAApZTXRadt27aKiIiQlLVPzogRI3JsCrps2TK9//77chzHLTsDBw687JhnzpzRkCFD3MULqlevrg4dOngbEQAAAEApdfnplcsIDAzUsGHDNHToUDmOo6VLl2rVqlVq2LChYmNjdfz48RyzOX379lXz5s1zHevcuXNatmyZ3nnnHZ04ccK95oknnlBgYKC3EQEAAACUUl4XHUnq1auXtm/frlmzZslxHJ0/f147duzIUXCMMWrQoIGef/75XMcYNGiQtm7dKo/HI2OM+z7PTTfdpPvvv78g8QAAAACUUl4/upZt+PDhevnll1WpUiVJ/1tGOvu4S5cumjdvnipUqJDr9fHx8crMzJT0v2LUpk0bvfHGGwWNBgAAAKCUKtCMTrb77rtPvf0VIXEAACAASURBVHv31po1a7Rjxw6lpqaqVq1a6tSpk5o0aXLZa8PCwhQdHS1jjEJDQzV48GA9+uijV1y0AAAAAAAuxWdtonz58urdu7d69+6dr+t69uyppk2bqmnTpoqMjFS5cuV8FQkAAABAKVXk0yb33XdfUUcAAAAA4GeKvOgUpR07dmjhwoXasmWLjh8/Lo/Ho+rVq+s3v/mNevXqpe7duysgoMCvMQEAAAAoZKWy6KSnp2vs2LH68MMPcyyeIElHjhzRkSNHtHLlSjVv3lwTJkxQgwYNiigpAAAAAG+UyqIzbNgwrVq1yv3ctGlTtWzZUpUqVdKhQ4e0Zs0apaSkaPv27fr973+vDz/8ULVq1SrCxAAAAADyo9QVnU8++cQtOYGBgRo/frzuuuuuHOfExMRo6NCh+uGHH3Tq1CmNGTNGb7/9dlHEBQAAAOCFUvcCyr///W/3eMiQIReVHEmKiIjQ66+/rjJlykiS1q1bp5iYmELLCAAAAKBgSlXRiYmJUXR0tKSs2ZwBAwZc8twaNWqocePGkrI2Pt23b1+hZAQAAABQcKXq0bWIiAh9//33OnDggGJjY1W1atXLnp+SkuIeZ8/uAAAAACj+SlXRkaTQ0FA1a9bsiuft3r3bnf0pW7asmjRpYjsaAAAAAB8pVY+u5YUxRuvXr9fjjz/ufjdo0CBVq1atCFMBAAAAyI9SN6OTm6NHj2rZsmWKiYnR5s2bdfjwYfe3u+++W08//XQRpgMAAACQXxQdSVu2bNEbb7xx0fePPvqonnrqKQUEMPEFAAAAlCT8H7ykY8eO5fr99OnTddttt+mbb74p5EQAAAAACoKiI6lXr15av369duzYoY0bN2rixImqW7eupKzH2gYPHqwNGzYUcUoAAAAAeWX90bWEhARt2LBB+/btU0JCgtLT0+XxePJ8veM4GjdunMWEUv369d3jq666Sn369FH37t318MMPa/v27crIyNDzzz+vNWvWqGzZslazAAAAACg4q0Vn2rRpmjFjRo79aLxhu+jkpkKFCpo8ebJ69uypzMxMnTp1SmvWrFGvXr0KPQsA/9FxwlolpqQXdYx8qRhSRl8N71bUMQAAyBdrRWfcuHF6//33ZYwp0DiO4/goUf7Vq1dPLVq00NatWyVJP/74I0UHQIEkpqQrISWjqGMAAOD3rBSd3bt3a+7cuZKyiooxRnXq1FGDBg1UqVIlBQYG2rhtnnk8HqWlpSkkJOSK50ZERLhFJykpyXY0AAAAAD5gpegsWrRIHo9HjuOocuXKmjx5sjp16mTjVvmydOlSvfXWWzp27Jhuv/12TZw48YrXxMfHu8dVq1a1GQ8AAACAj1hZde3C5ZjHjx9fLEqOlPXezaFDh5Senq6oqKgrvjuUlJTkzuZIUvPmzW1HBAAAAOADVorOiRMn5DiOatSooa5du9q4hVc6deqkihUrSpLOnj2rWbNmXfb8KVOm6Ny5c5Kk8PDwYlPYAAAAAFye1X10sveiKS5CQkL0pz/9yf38xhtvaMmSJRedl5qaqgkTJuiDDz5wv3vmmWfy9E4PAAAAgKJn5R2dGjVq6NChQzp//ryN4QvkkUce0datWxUVFaXMzEyNGDFCc+fOVceOHRUaGqqYmBitXbtWp06dcq8ZPHiw+vTpU4SpAQAAAOSHlaLTpk0bHTx4UHv37lVycrJCQ0Nt3MYrQUFBeuONNzRx4kTNmzdPxhjt3LlTO3fuvOjc0NBQDR8+XPfee28RJAUAAADgLSuPrmXPfqSnp2vOnDk2blEgwcHBGj16tJYvX65Bgwbp+uuvV4UKFVSmTBmFh4erffv2euaZZxQVFUXJAQAAAEogKzM6rVq1Ur9+/bR48WL961//0nXXXafu3bvbuFWBXHfddRo1alRRxwAAAADgY1aKjiS98MILSkhI0Jdffqm//OUv6tChg2655RY1atRIVapUydeL/REREbZiAgAAAPBDVorOPffcI0nyeDySJGOMvvnmmxz76+SV4zjatWuXT/MBAAAA8G9Wis6OHTvkOI4kuX/NZoyxcUsAAAAAcFl7dI1CAwAAAKCoWCk6a9assTEsAAAAAOSJlaJTq1YtG8MCAAAAQJ5Y2UcHAAAAAIoSRQcAAACA36HoAAAAAPA7Xr+jc/PNN7vHjuNo9erVuf5WUL8eGwAAAACuxOuic+zYMTmOI2PMRXvlZP9WULmNDQAAAABXUqBV1y63Vw776AAAAAAoKl4XnfHjx3v1GwAAAADY5nXR6du3r1e/AQAAAIBtrLoGAAAAwO9QdAAAAAD4HYoOAAAAAL9D0QEAAADgdyg6AAAAAPwORQcAAACA36HoAAAAAPA7FB0AAAAAfoeiAwAAAMDvUHQAAAAA+B2KDgAAAAC/Q9EBAAAA4HeCijqAJJ0/f16HDx9WmTJlVK9ePQUFFYtYAAAAAEoo640iLS1NW7ZsUeXKlfWb3/zmot8mTpyoRYsWKS0tTZJUvnx5Pfjgg3riiScUEhJiOx4AAAAAP2T10bXly5fr5ptv1h//+EetXLnyot8ff/xxffDBB0pNTZUxRsYYJScn691339XDDz+spKQkm/EAAAAA+ClrRWfx4sV69tlnderUKUnS0aNHc/z+ySef6Ouvv5YkOY6T4zdjjP773/9q3LhxtuIBAAAA8GNWik5iYqImTpwoY4wcx5ExRgEBOW81d+5cSVmlpmLFipo8ebKWLl2qxx57TAEBATLGaOnSpdq3b5+NiAAAAAD8mJV3dJYtW6aEhAQ5jqOwsDC9+eabatGihft7TEyMfvzxR3cm56WXXlKvXr0kSY0bN1a5cuU0depUGWP02Wef6frrr7cREwAAAICfsjKjk/1ImiSNGTMmR8mRpKioKElZszkVKlRQjx49cvz++9//3l2I4JtvvrEREQAAAIAfs1J09u7dK0kKCwtT165dL/p948aNkrLezenQocNFy0mXK1dODRo0kDFGx48ftxERAAAAgB+zUnTi4uLkOI5q16590W+ZmZnavHmz+7l9+/a5jlG5cmV3LAAAAADIDytFJzU1VZJy3Qfnxx9/zLFsdLt27XId49y5c5KkMmXKWEgIAAAAwJ9ZKTpVqlSRJMXGxl70W/Zja5JUo0YNNWjQ4KJzPB6PDhw4IMdx3LEAAAAAIK+sFJ1GjRrJGKMDBw5cVHZWr14tKev9nC5duuR6/ZdffqmEhARJYsU1AAAAAPlmpeh07txZUtbMzAsvvKD09HRJ0oIFC7Rnzx73vNtuu+2ia7dt26aXX37Z/dypUycbEQEAAAD4MSv76Nx5552aNm2akpOTtXbtWkVGRuqqq67Svn373L1zrr32Wt10003uNZs3b9bChQv1+eefu8WoUqVK6t27t42IAAAAAPyYlRmdatWq6cUXX3Q/nzlzRvv27ZOUtXdOYGCgXnrppRzXrF27VsuXL1d6erqMMXIcRyNHjnRXXwMAAACAvLJSdCSpd+/emjp1qsLDwyVlFRxjjK6++mq9+eabatu2bY7z69ev755Xvnx5jR8/XnfeeaeteAAAAAD8mJVH17Ldeuut6t69u3744Qf98ssvqlGjhn77298qMDDwonPr16+vG264Qd26ddP999/vFiQAAAAAyC+rRUeSAgMD1bp16yued+ONN2rJkiW24wAAAAAoBaw9ugYAAAAARcX6jM6vnTx5UidPntTZs2dVqVIltWjRQpJ0/vx5BQcH5/pYGwAAAADkR6EUnSNHjmjWrFlat26djh8/7n7fqVMnTZ8+XZK0Zs0ajR07Vvfee68effRRVaxYsTCiAQAAAPBDVh9dM8bo9ddfV8+ePTV//nzFxMS4q68ZY3Kce+zYMcXHx2v69Om688479eOPP9qMBgAAAMCPWS06Tz31lN5++21lZmZeVGyyNw7NduzYMff4+PHj+uMf/6iffvrJZjwAAAAAfspa0Zk2bZo+//xz93OzZs00cuRIzZs3L9fz+/btq5tuusndLDQhIUHDhw+3FQ8AAACAH7NSdE6cOOG+eyNJTz/9tD766CMNGjRIN954Y67XtGzZUjNnztSYMWPc2Z4dO3Zo/fr1NiICAAAA8GNWis6iRYuUmpoqx3HcxQXyqn///nrkkUfcz5999pmNiAAAAAD8mJWis3HjxqzBAwL017/+Nd/XDx48WMHBwZLEogQAAAAA8s1K0fn555/lOI4aNmyosLCwfF9fpUoV1a1bV8YYnTx50kJCAAAAAP7MStE5e/asJKlatWpej5FdkNLS0nySCQAAAEDpYaXoZG/2GRcX5/UY2TM5lStX9kkmAAAAAKWHlaJTs2ZNGWMUHR2tM2fO5Pv6w4cP69ChQ3IcRxERERYSAgAAAPBnVopOx44dJUkej0fvvvtuvq+fMmWKe9yuXTuf5QIAAABQOlgpOnfeeacCArKGnjNnjhYvXpznaydOnKgvvvjC/Xz77bf7PB8AAAAA/2al6DRs2FB9+/aVMUYej0ejRo3S3/72N3333XdKSUm56PykpCStXLlS99xzj2bNmiVJchxHPXr0UOPGjW1EBAAAAODHgmwNPHr0aO3Zs0c7d+6UJH3xxRc5Zmok6b///a9uvvlmnTx5UpmZmTl+q1Onjl555RVb8QAAAAD4MSszOpIUEhKiGTNmqHPnzjLG5PjjOI6krJmcY8eOKSMjw73OGKMbbrhB7733HiuuAQAAAPCKtaIjZW38OX36dE2cOFGNGjVyv7+w9Fz4XfXq1fXcc89pwYIFqlWrls1oAAAAAPyYtUfXLnTXXXfprrvu0pEjR7R161YdPnxYCQkJSk9PV+XKlVWjRg21bNlSTZo0cRcxAAAAAABvFUrRyVa3bl3VrVu3MG8JAAAAoBQq1tMnmZmZevvtt4s6BgAAAIASxkrRmTBhQoHH2L17t/r166fXX3/dB4kAAAAAlCZWHl2bNWuWHMfRc889l+9r09LSNG3aNM2cOVMZGRnuCm0AAAAAkFfW3tHJ3vgzP2Vn69atGjlypA4dOpRjRTYAAAAAyA+rixFkz+w8++yzlz3v/PnzmjJlij744IMcBScgIEAPPvigzYgAAAAA/JCVd3R69Ojh7pPz3nvv6dVXX73kuV999ZXuuOMOzZs3Tx6PR9L/Ng1duHChnn/+eRsRAQAAAPgxK0Vn6tSpuuuuuyTJLTuTJk3KcU5iYqJGjBihwYMH69ixY+73ISEheu6557Ro0SI1bdrURjwAAAAAfs7Ko2sBAQGaOHGiypYtq4ULF8oYo5kzZ0qSnnnmGX3xxRcaM2aMTp8+LWOMHMeRMUaRkZF68cUXFRERYSMWAAAAgFLC6js6r7zyisqVK6fZs2dLkmbOnKmNGzdq3759bsGRpLCwMI0cOVI9e/a0GQcAAABAKWG16EjSiBEjVK5cOb399ttyHEd79+7N8ft9992nYcOGqWLFirajAAAAACglrBcdSXrqqadUvnx5/b//9//cx9TKlSunmTNnqmXLloURAQAAAEApYmUxgtz86U9/cldQcxxHKSkpWr58eWHdHgAAAEApUmhFR5IeeughvfLKK+67OfPnz9fYsWMLMwIAAACAUsDrR9eaNGlS4JsbYzRv3jzNmzfvkuc4jqNdu3YV+F4AAAAASg+vi86Fy0LnV/aMTvZfvRkDAAAAAC6lQIsReFtQKDYAAAAAbPK66MyZM8eXOQAAAADAZ7wuOm3btvVlDgAAAADwmULZR6c4OnDggD766CNt3rxZR48eVVJSkkJDQ1WzZk21adNG9957r66//vqijgkAAADAC6Wu6KSnp2vSpEmaM2fORe8KnT17VmfPntWePXs0d+5cDRw4UMOHD1dwcHARpQUAAADgjWJddOLj47VkyRI98sgjPhnPGKNnnnlGK1eudL9r1KiRbrrpJlWtWlVnzpzR119/rX379rlLX58+fVpTp05VQEChbjkEAAAAoACsF52dO3dq48aNOnHihFJSUuTxeHJddc3j8SgzM1Pp6ek6d+6cTp06pQMHDsjj8fis6HzyySduySlTpozGjh2rPn36XHTeRx99pBdffFGZmZn6/PPPtXjxYvXv398nGQAAAADYZ63oJCUladiwYVq/fr3XY2Tv1eMr//73v93jYcOG5VpyJKl///6Ki4vTlClT3OsoOgAAAEDJYe15rGeeeUbr1q1zZ2+MMZf9k+3X34WGhvokz+HDhxUdHe2OOXDgwMue/8ADD6hMmTKSpEOHDikmJsYnOQAAAADYZ6XobN26VVFRUXIcR47jyBijevXqqXXr1qpRo4YkKSgoSG3atFGrVq3UoEEDBQYG5pjBiYiI0PTp07Vx40afZNqzZ497fMMNN1xxgYHQ0FCFh4e7n3/55Ref5AAAAABgn5VH1y582b9mzZr617/+pcaNG0vKev9l9OjRyszM1Lhx41SnTh1J0vnz5zVv3jy98cYbSk9P1/Hjx3X69GmFhIT4JFP37t21fv16/fLLL3l6HC4tLU1xcXHuZ1/NLAEAAACwz8qMzvfff+8ev/DCC27JkaQ2bdq4xxfO1pQrV06DBw/W9OnTFRAQIGOMJkyYoOTkZJ9kCgwM1NVXX63mzZurWbNmVzx/w4YNSklJcbNlFzIAAAAAxZ+VopP9mFelSpX0u9/9Lsdv11xzjSpUqCAp6xG3X2vXrp0efvhhSVJCQoI++eQTGxEvKy0tTa+//rr7OTIy0mczSwAAAADss1J0EhIS5DiOrr322lx/b9iwoYwx2r17d66/DxgwwH287Ntvv7UR8bLGjRunffv2ScqaCRoyZEihZwAAAADgPStFJzAwUNKl32u55pprJGWthObxeC76vVatWqpdu7aMMW7hKCxvvPGG5s+f735+4okndN111xVqBgAAAAAFY6XoVK1aVZIu+X5N7dq1JUkZGRk6cuRIrudkr84WGxtrIeHFjDGaNGmS3nzzTfe7bt26MZsDAAAAlEBWik5ERISMMTpw4ECuMzZ169Z1j/fu3ZvrGOfOncvxV5vS0tL07LPP5thQtFOnTpo6dapPNywFAAAAUDisLC/dunVrbdmyRYmJiVq9erV69OiR4/cL39357rvvdOutt+b4PS0tTUePHpUk64sAxMbGasiQITkWRujRo4emTJlyxb12gOKk44S1SkxJL+oY+VIxpIy+Gt6tqGOggPh3r2Qoif+cpNL5zwolQ0n8b6q0/fdkpejcfPPNevvttyVJo0ePVvny5dWpUyf390aNGikkJEQpKSn65JNP9Nhjj6l69eru73PnzlViYqIcx1GtWrVsRJQk7dq1S3/+858VExPjfvfggw9q5MiRCgiwMtkFWJOYkq6ElIyijoFSiH/3Sgb+OQG+xX9TxZ+V/5tv1qyZ2rdvL2OMzp49q0cffVT9+/fXgQMHJEnBwcG6+eabJWW9xzNgwAAtXLhQUVFRGjNmjCZPnuyOdeG+O760Zs0aDRw40C05AQEBGjFihEaPHk3JAQAAAEo4KzM6kjRmzBjdd999iouLkzFGO3bsyPG+zuOPP65Vq1bJ4/Ho2LFjevHFF93fjDGSslZvGzBggM+zLV68WKNGjXLzhIaG6rXXXlNkZKTP7wUAAACg8FmbuqhTp47ef/99tWjRIsd32a677jo9//zzMsa4L/xnF5xsw4YNU4MGDXyaa+nSpRo5cqRbcmrWrKn58+dTcgAAAAA/Ym1GR5IaNGigBQsW6Ntvv9WmTZtUtmzZHL8/8MADioiI0JQpU7R//35JWWWnVq1aGjp0qG6//Xaf5tm5c6dGjx7tFqoGDRrovffec5eyBgAAAOAfrBadbO3atVO7du1y/a1r167q2rWrjh07plOnTqlKlSruhqK+lJGRoaFDhyotLU1S1j49lBwAAADAPxVK0cmLWrVqWV1hbenSpTp06JD7uUWLFlqxYkWer+/Vq5dq1qxpIRkAAAAAXys2Rce2RYsW5fj8xRdf6Isvvsjz9U2bNqXoAAAAACVEqVlHee/evUUdAQAAAEAhsTKjM2LECJ+N5TiOxo0bV+BxfvjhBx+kAQAAAFASWCk6H3/8sbtktC/4ougAAAAAKD2svaPz6z1xruRSe+n4sjABAAAAKB2sFJ2+ffvm+dzU1FTFxcUpOjpap0+fzgoVFGRls1AAAAAApYOVojN+/Hivrtu0aZPGjh2r/fv366233tLs2bPVuHFjH6cDAAAA4O+K1apr7du31wcffKB69erp7Nmzevrpp90NPgEAAAAgr4pV0ZGkSpUqaeTIkZKkAwcOaNmyZUWcCAAAAEBJU+yKjiR16dJFYWFhkqRPP/20iNMAAAAAKGmKZdGRpHr16skYo59++qmoowAAAAAoYYpt0UlJSZEkxcfHF3ESAAAAACVNsSw6Z86ccWdyqlSpUsRpAAAAAJQ0xa7oGGP0j3/8Q6mpqXIch+WlAQAAAOSblX10YmJi8nyuMUYZGRlKSkrS/v37NX/+fG3bts39vUePHjYiAgAAAPBjVopOt27d5DhOgcZwHEfXXHON+vbt66NUAAAAAEoLK0UnmzEmX+c7juNeEx4ermnTpikwMNBGNAAAAAB+zFrRyW/JydawYUPdcsstevjhh1WpUiUfpwIAAABQGlgpOmvWrMnX+Y7jKDg4WJUqVVJwcLCNSAAAAABKEStFp1atWjaGBQAAAIA8KXbLSwMAAABAQVF0AAAAAPgdig4AAAAAv2PlHZ2HHnrIxrAXcRxHs2fPLpR7AQAAACg5rBSd7777rsAbhl6JMcb6PQAAAACUTIW2j86Fm4FeSnZx8XYPHgAAAACQLBWd5cuXS5KioqI0depUGWNkjFGrVq0UGRmpxo0bq0qVKipbtqySkpJ0+PBhbdmyRatWrdL58+flOI4iIiLUt29fG/EAAAAA+DkrRee6667TihUr9Prrr8sYo7p162rChAlq2bJlrue3bt1a/fr107PPPqvnn39eUVFRiomJUVBQkB5//HEbEQEAAAD4MSurrh09elSjR49WZmam6tatqw8++OCSJedCVatW1bRp09SxY0cZYzRt2jTt37/fRkQAAAAAfsxK0Zk/f777CNqYMWMUFhaW52sDAwP14osvynEcZWZm6v3337cREQAAAIAfs1J0NmzYIEmKiIhQ27Zt83193bp1df3118sYo6+//trX8QAAAAD4OStFJyYmRo7jqHbt2l6PUblyZUnSL7/84qtYAAAAAEoJK0Une3no2NhYr8f4+eefJUkVKlTwSSYAAAAApYeVolO7dm0ZY/TTTz/pwIED+b5+8+bN7qxQ/fr1LSQEAAAA4M+sFJ2bbrrJPX7hhReUlpaW52vj4+P1wgsvuJ979Ojh02wAAAAA/J+VonPfffcpMDBQkvT999/rkUce0ZEjR6543fbt2/XAAw/o4MGDchxH1atX1z333GMjIgAAAAA/ZmXD0Pr16+uhhx7Se++9J8dxtGXLFvXs2VPt2rXTjTfeqGuvvdZ99yYhIUH79+/Xpk2btG3bNneMwMBAjR07VuXKlbMREQAAAIAfs1J0JOnpp5/W0aNHtXr1andPnG+++UbffPPNJa/JXsQgKChIY8eOVefOnW3FAwAAAODHrDy6JmWVlX/+858aNmyYypcvL+l/RSa34+zPDRs21AcffKA+ffrYigYAAADAz1mb0ZEkx3E0ePBg9e/fX8uXL1fU/2fvvsOiOPo4gH+XKkWKooAIFhQLsRG7GLsmxoJGjBpjiTE2jBpjSbAk6it5NZrEEl9borHFFuyxJxhjQ8UgVhQpAkqRLnCUff/gYXMnBxx4Szm+n+fJk93b2Zk55/aG3+3szB9/ICgoCElJSQDyApz89Xbatm2Lt99+G927d5ezSkREREREVAXIGujks7S0xOjRozF69GgAQGpqKtLS0mBkZARLS0vo6cl2Y4mIiIiIiKqgMgl0XmVubs6FQImIiIiISDa8lUJERERERDqHgQ4REREREekcWYeu5ebm4uDBgzh//jyCg4MRHx+PzMxMlRnXiiMIAu7evStjLYmIiIiISNfIFug8f/4cXl5eCAoKAoASBTdERERERESvQ5aha7m5ufjss89w+/ZtBjhERERERFTmZLmjc+bMGdy4cQOCIEAURdjb22PIkCFo1qwZzM3NOZ00ERERERHJSpZA58iRI9J2165dsXbtWlSrVk2OooiIiIiIiAqQ5dbKw4cPAQD6+vpYtmwZgxwiIiIiIipTsgQ6MTExEAQBLi4usLW1laMIIiIiIiKiQskS6OTfwTE3N5cjeyIiIiIioiLJEug4ODhAFEVERUXJkT0REREREVGRZAl0unXrBgCIjIzE/fv35SiCiIiIiIioULIEOiNGjICJiQkAYOXKlVxLh4iIiIiIypQsgY6trS1mzZoFURRx6dIlzJs3D6mpqXIURUREREREVECp19E5dOhQkcctLCzQuHFjBAcH4+jRo/jzzz/RsWNHNGvWDJaWljA1NdW4LA8Pj9JWk4iIiIiIqqBSBzrz58+HIAjFphMEAaIoIjk5GWfOnMGZM2dKXBYDHSIiIiIiKolSBzr5inr+Jj8QUg6ISvq8jibBFBERERERkbJSBzp16tTRZj2IiIiIiIi0ptSBzvnz57VZDyIiIiIiIq2RZdY1IiIiIiKi8lShA52oqCgEBgaWdzWIiIiIiKiSee3JCNTp1asXAKB9+/bw8fEpVR6jRo1CQEAAnJyccOrUKW1Wj4iIiIiIdJwsgU5kZCQEQUBcXFyp8zA2NoYoinj+/LkWa0ZERERERFVBhRy6plAoEBYWVt7VICIiIiKiSqrUd3ROnDiBkJCQItOEh4dj3bp1JcpXoVDg8uXLiIqKAgDY2NiUtopERERERFRFlTrQsbOzw+zZsws9LooiwsPDsX79+tIWAUEQ4O7uXurziYiIiIioair10DU3NzcMHToUoigW+C+fumOa/gfkBVNeXl6v/y6JnyRZEgAAIABJREFUiIiIiKhKea3JCObMmYOoqChkZ2ervO7v7w9BEGBhYQEXFxeN89PT04OxsTGsrKzg4uKC4cOHw8LC4nWqSEREREREVdBrBTpWVlb4+eefC7zetGlTAEDLli2xefPm1ymCiIiIiIioxGSbdU15CBsREREREVFZkmUdndmzZ8PQ0BD9+vWTI3siIiIiIqIiyXJH5+rVq/jvf/+LPn36YNOmTXIUQUREREREVChZAp2HDx9CFEXk5OTAzc1NjiKIiIiIiIgKJUugk5iYKG03b95cjiKIiIiIiIgKJUugU7t2bWlbOeghIiIiIiIqC7IEOgMHDpS2t23bJkcRREREREREhZIl0JkyZQq6dOkCURSxY8cOLF++HC9evJCjKCIiIiIiogJkmV46PDwcc+bMgZmZGU6fPo0dO3Zg586dcHZ2RtOmTVGjRg2Ym5tDEASN8vPy8pKjmkREREREpKNkCXQGDBggBTGCIEAURYiiiODgYDx69KjE+THQISIiIiKikpAl0AEAURRL9HphNL3rQ0RERERElE+WQKddu3ZyZEtERERERKQRWQKdHTt2yJEtERERERGRRmSZdY2IiIiIiKg8MdAhIiIiIiKdU+EDnZiYmDItb/369WjSpAk+/PDDMi2XiIiIiIi0R7ZZ15TFxMTg2bNnyMzMRE5OjtqZ10RRRHZ2NrKysvDy5UvExsbi+vXruHjxIgIDA8uimrhx4wY2bNhQJmUREREREZF8ZA10Tp06hXXr1pVq7RwgL/gpq+mlg4KCMGXKFGRlZZVJeUREREREJB/ZAp1du3Zh2bJlAEq+dk5Z+/333+Ht7Y20tLTyrgoREREREWmBLIFOfHw8Vq5cWegdmfzXXg2AlNNaWlqiU6dO6NGjhxxVBACkpqZixYoV2Lt3r2xlEBERERFR2ZMl0Dl48CAyMjIgCAIEQcC4cePQt29f2NjYYM+ePfjpp59gYGAAX19fGBsbIzExEffv38eePXtw7949AIBCocD06dPRsGFDrddPoVBgz5492LBhAxISEgDkBVndu3fHH3/8ofXyiIiIiIiobMky69rVq1el7alTp2LevHlo06YNHB0d0bt3bwBATk4OwsLC4OTkhJYtW2L48OHYv38/RowYAQBIT0+Ht7e3HNXDhQsXsHz5cinIcXBwwJYtWzB+/HhZyiMiIiIiorIlS6Dz5MkTAIC+vj7Gjh2rcuyNN96AgUHejaRr166pHDMwMMCiRYvQtm1biKKIW7du4e+//5ajigAAU1NTTJ06FceOHYO7u7ts5RARERERUdmSJdBJSkqCIAho2LAhqlevrnLMyMgITk5OAIA7d+4UrJCeHqZNmybtnzlzRuv1s7S0hJeXF86ePYsZM2bA1NRU62UQEREREVH5keUZnczMTABAzZo11R53dnZGSEgIgoOD1R7v1KkTrKyskJSUJD2zo03t2rVDu3bttJ4vERERERFVDLLc0bGysgKQ9xyOOnXr1gUApKSkID4+Xm0aBwcHiKKIp0+fylFFIiIiIiLSYbIEOtbW1hBFEVFRUWqP5w9dA1DoXR0TExMAecEQERERERFRScgS6LRq1QoAEBkZidDQ0ALHlQOdwMBAtXnkB0nq1uEhIiIiIiIqiiyBTqdOnaTtxYsXIyMjQ+V4s2bNpO1jx44VOP/69euIioqCIAiFPudDRERERERUGFkmI+jbty9sbW0RExODa9euYeDAgRg/fjwGDRoEc3Nz1KhRAy1btkRgYCCCg4OxePFizJs3D6ampggKCsIXX3wh5eXq6ipHFakK6/LNeaRkZJV3NUqsejVD/D2/Z3lXg6hKqIzfE1X1O4JtRUSFkSXQMTQ0xOzZszF37lwIgoCIiAgsXboUrq6u0rC2Dz74QBq2tm/fPvj6+sLExATJyckqeQ0YMECOKlIVlpKRheSM7PKuBhFVYPyeqDzYVkRUGFmGrgHAoEGDMHfuXOjr60uvOTo6StuDBw9G586dIYoiAEChUCApKUnaB4D27dujX79+clWRiIiIiIh0lGyBDgB89NFHOHToEDw8PGBvb48aNWqoHF+7di369u2rEtwAgCiKcHd3x/r16+WsHhERERER6ShZhq4pa9SoEb755hu1x8zMzLBmzRrcvn0bfn5+iImJgbW1Ndzd3bmgJxERERERlZrsgY4mWrRogRYtWpR3NYiIiIiISEfIOnSNiIiIiIioPJTpHR2FQoGgoCA8f/4cSUlJqFWrFnr16gUASEhIgCiKBZ7jISIiIiIiKqkyCXSuXLmCn3/+GZcvX0ZW1r9z3bu7u0uBjp+fHxYsWIB+/fph5syZKjO0ERERERERlYSsQ9cyMjIwd+5cjB8/HhcuXIBCoYAoitJ/yiIjI5GdnY0TJ07Aw8MDf/75p5xVIyIiIiIiHSbbHR2FQoGPP/4YN27cKBDUAIAgCCr7kZGR0nZaWho+/fRTbNu2DW5ubnJVsYAOHTrgwYMHZVYeERERERHJQ7Y7Oj4+Prh+/bq0369fP/z444/w8/NTm37y5Mnw9PSEIAgQBAEKhQJffvklsrO52jEREREREZWMLIHO48ePsW/fPgCAvr4+Vq9ejR9++AE9e/aEra2t2nOcnJywdOlSbNy4EUZGRgCAsLAwnD59Wo4qEhERERGRDpMl0Dl06BBycnIgCAImTpyI/v37a3xu165d4eXlJe2fOXNGjioSEREREZEOkyXQuXTpEgDAwMAAEyZMKPH5o0ePhomJCQDgzp07Wq0bERERERHpPlkCnaioKAiCABcXF5ibm5f4fBMTE9SrVw+iKCI2NlaGGhIRERERkS6TJdBJTU0FAFSvXr3UeVhYWAAAcnJytFInIiIiIiKqOmQJdCwtLQHgte7GPH36FABgZWWllToREREREVHVIUug4+joCFEUERISgqioqBKff/fuXWn4m5OTkww1JCIiIiIiXSZLoOPu7i5tf//99yU6NycnBz4+PtJ+x44dtVYvIiIiIiKqGmQJdIYMGSKthXP06FGsWbNGo/MyMjIwZ84c+Pv7A8hbg2fw4MFyVJGIiIiIiHSYLIFOnTp1MHbsWIiiCADYsGEDhg0bBl9fXzx+/FglbW5uLh4/fowtW7bg7bffxu+//w4AEAQB7733HhwdHeWoIhERERER6TADuTKeNWsW7t27h4sXL0IQBNy5cwdffvmlSporV66gZcuW0sxqoihCEAQAgKurK7y9veWqHhERERER6TBZ7ugAgJ6eHn788UcMHz4coihKd3eUg5ns7GxkZ2dLx/KPd+/eHT/99JM0/I2IiIiIiKgkZAt0AMDIyAhLlizBzp070aNHD+jr6wOAFPgoBzgA0Lp1a6xZswb/+9//pHV0iIiIiIiISkq2oWvK2rZti7Zt2yIzMxOBgYEICwtDcnIysrOzYWFhAVtbW7Rp04Zr5hARERERkVaUSaCTz9jYGO3atUO7du3KslgiIiIiIqpiZB26RkREREREVB4Y6BARERERkc5hoENERERERDqHgQ4REREREekcBjpERERERKRzGOgQEREREZHOYaBDREREREQ6h4EOERERERHpHAY6RERERESkc2QJdBISEpCTkyNH1kRERERERMWSJdBZu3Yt3N3dsXTpUty7d0+OIoiIiIiIiAolS6Dj7++PhIQE7N69G1evXpWjCCIiIiIiokLJEuhER0dL22+//bYcRRARERERERVKlkBHFEVp29LSUo4iiIiIiIiICiVLoNOqVStp29/fX44iiIiIiIiICiVLoOPl5QVDQ0MAwLJly/D8+XM5iiEiIiIiIlJLlkDHzc0NmzZtgrW1NSIiItC/f38sXboUp06dQnh4OFJTU+UoloiIiIiICABgIEemM2bMAAA4OzvD398faWlp2L17N3bv3l3ivARBwN27d7VdRSIiIiIi0mGyBDqnTp2CIAgAIP0fUJ2kgIiIiIiISC6yBDoAgxoiIiIiIio/sgQ6v/zyixzZEhERERERaUSWQKd9+/ZyZEtERERERKQRWWZdIyIiIiIiKk8MdIiIiIiISOfINhnBq65cuYLz58/jxo0biImJQWJiIvT19WFmZgYHBwc0b94c3bp1Q/fu3VVmaiMiIiIiIiop2QOd+/fvY+HChQgKCpJey5+RLSsrCxkZGXjx4gVu376NvXv3wsnJCUuXLuVzPkREREREVGqyDl07efIkRowYgaCgIIiiKP33qvzXRFFEWFgYPvroI/z2229yVo2IiIiIiHSYbHd0bt26hblz50KhUEhD0WxsbNChQwc0btwYFhYWyMnJQVJSEoKDg+Hv748XL15AEARkZ2dj0aJFcHJyQtu2beWqIhERERER6ShZAh1RFLFgwQIpyDEzM8P8+fPh4eEBAwP1RWZlZeHQoUNYsWIFUlNTkZ2djcWLF+PYsWN8ZoeIiIiIiEpElqFrp06dwqNHjyAIAqpXr45du3Zh2LBhhQY5AGBoaAhPT0/s2rULFhYWAICQkBCcO3dOjioSEREREZEOkyXQUQ5O5syZgyZNmmh8rouLC+bMmSPtnzx5Uqt1IyIiIiIi3SdLoBMYGAgAMDMzg4eHR4nPHzx4MMzNzQEAt2/f1mrdiIiIiIhI98kS6MTFxUEQBDRq1AiGhoYlPt/Q0BCNGjWCKIp4/vy5DDUkIiIiIiJdJkugk52dDQClCnLyvc65RERERERUtckS6FhZWUlr4pRWaGgoAMDa2lpLtSIiIiIioqpClkCnWbNmAIDY2Fhcu3atxOdfuXIFsbGxEAQBjRs31nb1iIiIiIhIx8kS6HTt2lXaXrZsGdLS0jQ+NzU1Ff/5z3+kfXd3d63WjYiIiIiIdJ8sgY6HhweqV68OAAgODsaECRPw9OnTYs+LiIjAhAkTEBwcDACwsLDA0KFD5agiERERERHpsMJX8HwNZmZmmDt3LhYuXAhBEHDr1i2888476NmzJ9zd3eHs7AxLS0sAQFJSEh49eoSLFy/i/PnzyMnJAQAIgoBPP/1UmmaaiIiIiIhIU7IEOgDg6emJBw8eYOfOnRAEAVlZWTh9+jROnz5d6DmiKEIQBADA8OHD8cEHH8hVPSIiIiIi0mGyBToAsGDBAtSpUwfr1q3Dy5cvpUBGFEWVdMqvGRoa4rPPPsO4cePkrBoREREREekwWQMdAPjoo4/g4eGBX375BX/99Rfu3btXINDR09NDkyZN0K1bN4wePRo1a9aUu1pERERERKTDZA90AKBGjRqYOXMmZs6ciZcvXyI+Ph4JCQkQRRGWlpaoXbs2TE1Ny6IqRERERERUBZRJoKPM1NQUpqamcHR0LOuiiYiIiIioipBlemkiIiIiIqLyxECHiIiIiIh0TqmHrvXq1UvaFgQBZ8+eVXtMG/T19WFsbAwbGxs4OzujV69e6NSpk1bLICIiIiIi3VHqQCcyMlKaFjp/7ZtXj2nbo0ePcOXKFezatQtvv/02Vq1aBT093pQiIiIiIiJVrxUlvDpN9KvH5Pzv5MmT2Llz5+tUn4iIiIiIdFSp7+j4+PiU6lhp5ObmIj09HZGRkThx4gRiYmIgiiJ8fX0xZswYrZZFRERERESVX6kDnSFDhpTq2OsaMWIE+vXrBwAIDQ2VrRwiIiIiIqq8Kt0DLvXq1YOFhYX0fBAREREREdGrKl2gk5qaipSUFIiiCFtb2/KuDhERERERVUClHrpWFkRRxOnTp6WhagBgbm6OK1eu4NatW3j58mU51o6IiIiIiCoq2QOdFy9e4NKlS3j27BkyMjKQm5urdshZbm4ucnJykJWVhZcvXyI2NhYBAQFITk7GvXv3VNJaWlqiW7duclediIiIiIgqKdkCnezsbPj4+ODXX39Fbm5uqfJQt0YPERERERFRcWQLdL766iscPHhQuntT1OQB+cGMuuNcEJSIiIiIiEpKlkDnwYMHOHjwIIB/AxxjY2NYW1sjOTkZaWlp0NfXh62tLXJycpCcnIz09HQpPQDY29tj4sSJ6N69uxxVJCIiIiIiHSbL7ZKjR49Kd2csLS2xYcMG3Lx5E3/88QdmzZoFIO/uza+//go/Pz8EBATg5MmTGDlypHQsOjoaderUgb29vRxVJCIiIiIiHSZLoHP9+nVp+8svv0SPHj2kIWgdOnQAkBfMXLx4UUpXv359LF68GCtWrJCOf/3111AoFHJUkYiIiIiIdJgsgU5UVBQAwMTEBO+++67KsUaNGsHExAQAcOPGjQLnDhw4EMOGDQMAREdH48SJE3JUkYiIiIiIdJgsgU5ycjIEQYCzszP09fVVjgmCgIYNG0IURdy9e1ft+ePGjZO2le/6EBERERERaULWKc0sLCzUvl6/fn0AwJMnT9Qeb9SokfRszoMHD2SpGxERERER6S5ZAh1LS0sAkGZSe5WTkxMAIDMzE0+fPlWbxs7ODqIoIiYmRo4qEhERERGRDpMl0MkPUkJDQ9Ued3R0lLYfPnyoNk1OTg4AIC0tTev1IyIiIiIi3SZLoOPm5gYASEhIwOXLlwsczx+6BqjO0JYvNzcX4eHhAAAjIyM5qkhERERERDpMlkCnW7du0ra3tzcePXqkcrxZs2YwMMhbq/TIkSMF7tocOXIEiYmJEAQBdnZ2clSRiIiIiIh0mCyBTseOHeHq6gogb6ppDw8PzJw5E5GRkQDypp12d3eHKIqIj4/HuHHjcOnSJTx8+BA//fQTFi9eLOWVf3dIDomJidiwYQPef/99tG3bFm+88QZ69OiByZMn4/jx48jNzZWtbCIiIiIiko+BXBl//fXX+PDDD5GRkYHs7GycOnUKkydPlo5/8skn+PPPPwEAQUFBmDBhgnRMFEVpe/jw4bLU78qVK/jss88QHx+v8npUVBSioqLwxx9/YNeuXVi9ejXvKhERERERVTKyTS/9xhtvYNOmTahbt670mvIkBG5ubvj444+loEYURWlbEAQAeevptGzZUut1CwwMxCeffCIFOQ4ODhg7dixmzJiBAQMGoFq1agDyFjSdOHEiUlJStF4HIiIiIiKSj2x3dACgXbt2OH78OI4dO4arV6/CzMxM5fjnn38OOzs7rF27FklJSQDyAh4TExNMmTIFn3zyidbrpFAoMGfOHGRmZgIAhg4diq+//lpl0oPw8HBMnjwZjx8/xsOHD7Fq1Sp89dVXWq8LERERERHJQ9ZAB8ibNW3o0KEYOnSo2uOjR4/G8OHDcevWLcTGxsLKygpubm4wMTGRpT6+vr7StNcuLi5YtmwZ9PX1VdI4OTlh8+bN6N+/PzIyMrB//35MmDBB5Y4UERERERFVXLINXSsJIyMjtG/fHu+++y66dOkiW5ADAAcOHJC2J02aVCDIyefg4ABPT08AQHZ2No4ePSpbnYiIiIiISLsqRKBTVhISEnD79m0AgL6+Prp27Vpk+u7du0vbp0+flrNqRERERESkRVUq0Ll796404YGzszMsLS2LTN+6dWtp+8GDB3j58qWs9SMiIiIiIu2Q9RmdjIwMHD16FJcuXUJYWBhSU1ORk5NTojwEQcDZs2e1Up8nT55I28qzwRXG3NwclpaWSEpKQm5uLsLDw9G0aVOt1IWIiIiIiOQjW6ATFBSE6dOn49mzZ6XOQxRFaappbYiJiZG27e3tNTrH1tZWmhEuNjaWgQ4RERERUSUgiMqrc2pJXFwcBg4ciISEhNfOSxAE3Lt3Twu1ylvEdPfu3QCAjz/+GHPmzCn2HE9PTwQGBgIAvvvuO/Tv318rdSlM586dCyxiampqKmuZVc1LRQ4ArX/sy4AAUyP1k2cAlfV9Ff2edJEuthPfU0Whi+8J0M33VfXeky5iO5Xcq4+BPHjwQNbyZLmjs23bNiQkJEAQBIiiiKZNm6J79+6wtbWFsbGxVu/SlIRCoZC28xcFLY5yOuXz5ZKenl7gNT4bRPleZpd3DbRPF9+TLtLFduJ7qjx08X3xPVF5qUrtJEug4+fnJ22PHDkSixcvlqOYEtPT+3fuBU2DLeUbXsrnExERERFRxSVLoBMZGQkg72H+efPmyVFEqSgPAcvIyNDonMzMTGnbyMhI63V6lYmJSYE7OBy6RkRERESVnfLfuGUxwkuWQCd/EgEXFxeNh4iVhdIEOsrpLCwstF6nV126dEn2MoiIiIiIdJ0sY7EcHBwAoMRTScvN1tZW2n7+/LlG5yinq1WrltbrRERERERE2idLoOPu7g5RFHH//n2N75yUhcaNG0vb+cPripKamipNLa2vrw8nJyfZ6kZERERERNojS6AzYsQIGBkZITMzE5s3b5ajiFJp0qSJNKHAw4cPi53NLCAgQNpu3LgxjI2NZa0fERERERFphyyBTv369fH5559DFEVs2LAB27dvl6OYEjM3N0e7du0AAFlZWfjrr7+KTP/HH39I22+99ZasdSMiIiIiIu2RZcHQfFu3bsW3334LAKhTpw66deuGBg0awNzcvEQzLXh4eGitTvv378eCBQsA5N3hOXDggNrZ1J4+fYr+/fsjMzMTgiDg999/R4MGDbRWDyIiIiIiko9sgY4oiti5cye+++47aYhYaaeRu3fvntbqpVAo4OHhgcePHwMA+vTpg//+978wMzOT0kREROCTTz5BSEgIAGDw4MFYsWKF1upARERERETyki3QWbRoEfbv31/g9ZIWJwiCVgMdIO/Zm7Fjx0pr5NSqVQv9+vWDjY0NQkJCcObMGaSnpwMAHB0dceDAAVhZWWm1DkREREREJB9ZAh0/Pz9MmjQprwBBkNbVqVGjRqke6D9//ry2q4jLly9j9uzZiI+PLzRNs2bNsGHDBtjb22u9fCIiIiIiko8sgc60adNw7tw5CIIAGxsbzJkzB71791ZZsLMiSElJwZ49e3Du3DmEhoYiNTUV1atXR/PmzdG/f394eHjAwECWNVWJiIiIiEhGsgQ6vXr1QmRkJPT09ODr64smTZpouwgiIiIiIqJCyTK9dFxcHARBgIuLC4McIiIiIiIqc7IEOhYWFir/JyIiIiIiKkuyLRgqiiKePn0qR/ZERERERERFkiXQGTBgAAAgOjoa169fl6MIIiIiIiKiQskS6AwaNAhOTk4QRRGLFi1CcnKyHMUQERERERGpJUugY2Jigm+//Rbm5uZ48uQJPDw8cOjQIbx8+VKO4oiIiIiIiFTIMr303r17AQD379/Hnj178goSBAiCAHt7e9jY2MDIyAiCIBRfQUHA9u3btV1FIiIiIiLSYbIEOk2bNi0QxCgXo0mAk3+OIAi4d++eVutHRERERES6zUCujIuKn2SIrYiIiIiIiCSyBDpeXl5yZEtERERERKQRWYauERERERERlSdZZl0jIiIiIiIqTwx0iIiIiIhI58g2GQFVPuvXr8eaNWvQvn177NixQ6NzgoODsXfvXly5cgXR0dHIzMxE7dq10apVKwwePBjdu3fXuPz09HTs378fZ86cwcOHD5GWlgZra2s4Ozujf//+8PDwgJGRUZnnpYvKu62jo6Oxf/9+XLlyBU+ePEFKSgqMjY1ha2uLN998E0OGDIGbm1ux+axYsQJbt27VuNzGjRvj2LFjGqfXBeXZ1jNmzMDJkyc1rmv37t2xcePGQo9nZ2fjyJEjOH78OO7evYuUlBRYWlrCyckJ/fr1w3vvvYfq1atrXJ6uKeu2vnr1KsaMGVPq+g4ZMgTffPNNgde1/bmpzEJCQrB//374+/sjIiICqampMDMzg729Pdq1a4fhw4fDxcVFo7wqah/L/jpPRW3ryt5f8xkdAgDcuHEDY8eORVZWlkadZHZ2NlasWIEdO3YgNze30HRubm5YuXIl6tatW2R+Dx48wLRp0xAREVFomsaNG+O7775D48aNyywvXVTebb1p0yasWbMGWVlZRabr168fli9fDnNz80LTjBs3DpcvXy4yH2VVLdAp77bu06cPwsPDNa5vUX+wPnv2DFOnTsWdO3cKPd/e3h7ffvst2rZtq3GZuqI82vp1A52hQ4fCx8enwOva/NxUVllZWVi5ciV++eWXImeqFQQBo0aNwvz584v8w7Wi9rHsryt2W+tCf81AhxAUFISPPvoISUlJAFBsJymKIqZPn44zZ85Ir9WuXRu9evWCnZ0dYmJicPbsWTx//hwAUKtWLezYsQMNGjRQm19ERASGDRuGxMREAICNjQ369u2L2rVrIyIiAmfOnEFycrJUzr59+2Bvby97XrqovNv61V906tWrh65du6J27dpITk6Gv78//vnnH+l4mzZtsH37dhgbG6vNr3379khKSoKJiQmmT59e7Pu3trbG0KFDi02nC8q7rZOTk9GuXTsAgK2tLcaOHVtsnZ2cnNCnT58CryclJWHYsGHSH7/Vq1dH37594ejoiJiYGJw5cwaxsbEAAFNTU+zevRvNmjUrtjxdUV5tHR0djRMnTmhcz2vXruHPP/8EAFSrVg2//PILWrVqpZJGm5+bykoURcyaNQu///679FqTJk3QuXNnWFtb48WLF7h06RIePnwoHe/Xrx++//576OkVfCKhovax7K8rdlvrTH8tUpV24sQJsU2bNqKLi4v03+jRo4s8Z/PmzSrply5dKmZkZKikycjIEBcvXiylGTBggJiZmak2v9GjR0vpJkyYIKakpKgcj4uLE0eOHCml+fjjjwutmzbz0jXl3db+/v4qeW3evFnMyckpkO7cuXNi69atpXSrV69WW7fw8HApzfvvv1+CfwndV95tLYqieOnSJSndZ5999lrvZ+7cuVJeQ4YMEWNiYlSOp6amil5eXir1UvfZ0kUVoa018fjxY/HNN9+U8jt69KjadNr83FRWvr6+0r+Bq6ur6Ovrqzbdvn37xGbNmklp9+3bpzZdRe1j2V9X3LbWpf6akxFUUampqVi0aBFmzpyJtLQ0jc97+fIlNm3aJO17enpiwYIFBSJ4Y2NjfPXVV+jduzcA4OHDh/j111+h4pdkAAAgAElEQVQL5Hfx4kVcu3YNAFCzZk388MMPBW591qxZExs3bkStWrUAABcuXMD169dlzUuXVJS23rx5s7Q9ZswYfPzxx2p/kerZsyeWLVsm7e/YsQMKhaJAuqCgIGn7jTfe0Ph96bKK0tYAVIaYtWjRoiRvQ0VISAgOHz4MADAyMsK6deuk6zefmZkZVq1aJd3FefjwIY4ePVrqMiuDitTWxVEoFJg1axZSUlIAAO+//z4GDBigNq22PjeV2ZYtW6Ttzz//HB4eHmrTeXp6YubMmWrPy1dR+1j213kqalvrUn/NQKeKUSgU2L59O3r37o29e/cCyBv32aNHD43Ov3z5sjQ8wtzcHHPnzi0y/WeffSZt79mzp8DxAwcOSNtjx46FmZmZ2nyqV6+OCRMmSPu+vr6y5qULKlJbp6en4++//5b2J06cWGRe/fv3R+3atQEAaWlpKrfH8/EPon9VpLbOp9w+r9OxHTx4UBq3PmTIENSpU0dtOiMjI0ydOlXa53WtnhxtXZzvvvsO9+/fBwA4Ojriiy++KDSttj43lVVYWBiCg4MB5AXwo0aNKjL9Bx98AENDQwBAaGgooqKiVI5X1D6W/XXFbWtd668Z6FQxFy5cwPLly5GQkAAAcHBwwJYtWzB+/HiNzr99+7a03aVLF1hYWBSZ3tnZWRr/GRISovJwXG5ursrFVNxMTsrHz549q/LQnjbz0hUVqa0fP34sPcxYp04d6UuxMIIgwMnJSdqPiYkpkKaq/0GkrCK1db789tHT00Pz5s01qoc6Fy5ckLaLu67d3d2lPwSuXbsmjVPXJRWxrYty584dbN++XdpfunQpTExMikwPvP7nprLKDwgBoHnz5sXOjGVmZgYbGxtpX/m7sqL2seyv81TUtta1/prTS1dRpqamGDduHCZOnAhTU1NcvXpVo/NevHghbdevX1+jc+zs7BAdHQ0gr5N1dHQEAISHh0sPxZmamqJJkyZF5tOgQQNYWVkhMTERiYmJCAkJgbOzs9bz0jUVoa2bN2+Ov/76C7GxsUhPT9cor/wHoQGo/WXq7t270rGGDRsiKysLQUFBePToETIyMlCzZk20bNmy2JnBdElFaGsASElJkSYOcHZ2hqmpKTIyMnDr1i2EhYUhOzsbNjY2cHNzKzAMTVlmZiYeP34s7bdp06bIOpmamsLFxQV37txBTk4OAgMD8dZbb2n0fiqbitLWRRFFEV9//TVycnIAAO+++y46depUaHptfW4qs969e8PPzw8xMTEQBKHY9AqFQgp6AdXvyorax7K/zlNR21rX+utSBzpffPEFFAoF2rdvj/fff1+bdSIZWVpawsvLC6NGjULNmjVLfL7yLw6aXJhA3h8r+Z4+fSptP3nyRNrWtOOsU6eO9CttaGiodGFqMy9dUZHaWk9PD7Vr1y72l6F89+7dU/nl+NUpMCMiIqS2a9CgAdavX4/du3cjPj6+QF6tW7fG559/Ls3kpIsqUlsDeb/e5f9CWLduXfj4+ODgwYPSMxrKZXXp0gVz5sxB06ZNC5QRFhYm/ZFsbm4Oa2vrYutVp04d6dfD0NBQnQt0KlpbF+X48ePSMJZq1aphzpw5RabX1uemMtPX14ednR3s7Ow0Sn/hwgVkZGQAAExMTFT6v4rax7K/zlNR21rX+utSBzqXL1/G8+fP8eTJkwKBzrp16wDkTUU3cODA16shaVW7du1e6wNka2srbeePLS1KdnY2QkNDpf24uDhpW/n2pqZTRtra2kq/DORPJ6vtvHRFRWrrkvr222+l7RYtWsDBwUHluPJt8KCgIJUHHV9169YtjBkzBp9//rnKuGRdUtHaWrl9/vjjj0LzEUURFy9ehL+/P/7zn/8U6C9Ke13n43VdUFld16IoSn8LAMCoUaOKbUNtfW6qCoVCgR9++EHa79atG6pVqybtV9Q+lv11yZVlW5dURe+vSx3o5Edn6sb3rlu3DoIgwN3dvcp+AemqDh06SNt//fUXoqOji7yoTp06hZcvX0r7+b9GAFD5la6oMdvKlC9s5fO1mRfl0WZbl8SmTZtw8eJFaf/TTz8tkObVRSMdHBwwYcIEdO/eHbVq1UJiYiIuX76MjRs34vHjx8jNzcWKFStgYWEBT0/PUtVLl2m7rV9tHxcXF0yYMAGdOnWCtbU1YmNjceHCBWzcuBHR0dHIzMzEvHnzYG1tDXd3d+k8XtfaV1bX9blz56RfmU1NTYt9oBnQ3uemqli+fLm0voq+vj68vLxUjlfUPpbXdcmVZVuXRGXor0s9GUH+SsrK4wVJ97Vs2RKurq4A/p0yNDU1VW3aiIgILF++XOW17OxsaVt5CkLlC64oyumUz9dmXpRHm22tqf3792P16tXS/pAhQ9QOPVL+4uzYsSMOHz6MDz74AA4ODjAyMkLt2rUxePBg/Pbbb+jatauUdvny5Wpvl1d12m5r5fYZMGAADh48CA8PD9ja2sLIyAgODg4YOXIkfH19pQfOc3Jy4O3tzetaZmV1XStPf+vp6YkaNWoUe462PjdVwZo1a1RmwZsyZUqBIUMVtY/ldV0yZd3Wmqos/XWpAx0bGxuIoojg4GDp1hdVDQsXLpRmNgoICMDQoUNx6NAhxMbGIisrC5GRkdi2bRuGDRuGuLg4lduYyuPDledk13SsuPLMIMrnazMv+pe22loTO3fuxMKFC6V2adGiBb766iu1adesWYPDhw9j/fr1WLt2LapXr642XbVq1bB69WpYWVkByFtDZPfu3SWqV1Whzbbeu3cvDh48iHXr1sHHx6fQz4K1tTV++OEHGBjkDS549uwZjh07Jh0vzXXJ67p4cl/XAQEBCAgIAJD36/OYMWM0qpe2Pje6TBRFrFy5EuvXr5de69mzZ4Ff+IGK28eyv9ZMebW1JipTf13qoWuurq6IiopCbm4uRowYgXbt2qFWrVoq/8APHz4scr58TQiCUOAXJSpfbdq0wcqVKzFv3jxkZmYiLCwM8+bNU5t28ODBaNasGb755hsAqrdUTU1NpW1Nh0MoPxSr3AlqMy/6l7bauiiiKGLVqlUqC5Q1b94cW7ZsKfSXKXNzczRt2lSjB5EtLCwwePBgaYrbv/76C9OnT9eoblWJNtvaysoKVlZWGk0j6uTkhO7du+Ps2bMA8tpn6NChAFSva+XrtSi8rosn93X922+/Sdtdu3bVeCYlbX1udJVCoYC3tzeOHDkivebu7o7vv/9e7R+3FbWPZX9dvPJs66JUxv661IGOp6cnzpw5A0EQoFAocOnSJZXjoigiJiYGhw4deq0KAmCgUwG98847qF+/Pr755htcuXKlwPF69eph6tSp8PDwwJo1a6TXlWcJUr4wNZ3CUPkCVn4+TJt5kSpttHVh0tLSMHfuXOmPFQBwc3PDxo0btdomrVu3lr44w8LCtJavrpGzrYvSqlUr6TOg3D6ve10X9sshydfWOTk5OHfunLT/3nvvaa/Sryjsc6OL4uPj4eXlhZs3b0qv9e3bF6tWrSr0j9SK2seyvy5aebd1YSprf13qQOett97ClClT8L///Q8A1C7gpI1FnTS9BUdlr1mzZti+fTuioqIQEBCAFy9ewMLCAg0aNEDLli2ldCEhIdK28qrmyrP/qFtgSh3ludqV11HQZl5U0Ou2tTpPnz7F1KlT8eDBA+m1vn374ttvv4WxsbFW629paSltp6WlaTVvXSNHWxcnf6gCoNo+r3tdazo9alUlR1tfv35dGldvbm5e7MKFr6Owz42uuXv3LqZNm4aoqCjptdGjR8Pb27vIIUcVtY9lf124itDW6lTm/vq1FgydMWMGevbsCT8/P8TFxUm3wXx9fSEIAmrXro3OnTu/diWpYqtTp06RnZ/yStzKi1i5uLhI25GRkRqVpZyuQYMGsuRFhSttW78qICAAU6ZMUZnMZMKECZgzZ06JftxIS0tTuzjZq5Qfttb1XwO15XXbWhRFpKenq/zSWJjC2qdevXowNjZGZmYmEhMTkZqaCnNz8yLz4nVdctq6rgHg9OnT0nb37t1LPMxIG58bXXLu3DnMnj1b+pVeT08P8+bNw7hx44o9t6L2seyv1asobf2qyt5fv1agA+Q9gNSiRQuV13x9fQHk/cP7+Pi8bhFUwSgUCsTFxSEjIwMNGzYsMu39+/elBeZsbGxULiZbW1tYW1sjISEBiYmJCA0NLXKl7pCQECQlJQHIi/idnJxkyYv+pa22VnbhwgVMnz5dum1uaGiIr776CsOGDdOoTv7+/pg1axYSExNhYmKCa9euFftle//+fWm7UaNGGpVT1WirrY8dOwYfHx8kJCSgcePGOHz4cLFlF9Y++vr6aNSokTRrzz///IMuXboUmk9aWpq0Noyenp40MxepkuO6zqc81Wy3bt00rpM2Pze64uDBg1iwYIE0y62ZmRm+++47jf9dK2ofy/66oIrU1sp0ob+WbeoKbQxbo4onMTERLVq0QI8ePTB69GjpoizMvn37pO3evXsXuPXao0cPafv8+fNF5qW8gJy7u3uBi0WbeZH22xrIW2h42rRp0pempaUltm7dqvGXJpC36nP+7FDJycm4ceNGkelzc3Nx4sQJab8qrrdRHG22tZ2dHeLi4pCTk4MHDx4U++tiamqqyvWoPL0okDfLUL7iruu//voLWVlZAPKmUVYe2kR55Liu88XFxaksLtqmTRuN66Xtz01ld+jQIXh7e0vtY29vjz179pQoeAQqbh/L/vpfFbGtAd3pr2UJdLy8vODl5cXFQnWQlZWVNH97fHx8katX37lzR+ok9fT01E4xOmjQIGn7p59+QnJystq8kpOT8fPPP0v76i40beZF2m/r6OhozJw5U5qvv1atWti1a5fKAoaasLOzU/kDas2aNUX+sLJlyxbpjy8TExMuGKqGNtvazc0NdnZ2APJ+8Fq7dm2RZa9cuVK6Vu3s7NCnTx+V4wMHDpQ64QMHDkh3F16lUCjw448/Svu8rtXT9nWtTHmIW61ateDo6KhxvbT9uanM7ty5ozJ1r7OzM/bu3VvssEF1Kmofy/46T0Vta13qr2UNdJT/0Ul3vP/++9L2kiVL8OjRowJprl27hk8++UT6dfXDDz+Es7NzgXSdOnVCp06dAACxsbGYNGlSgQWi4uPjMXnyZMTGxgIA2rZtq/bZL23mRXm02dbz589HYmIigLzb8lu3bi2w6JmmlFdfvnr1Kry9vQtMP5yTk4MtW7aoLGg2e/ZsWFtbl6pMXaetttbT01NZ58HX1xc//PADcnJyVNIpFAr4+Pjg119/lV5btGiRtL5Lvnr16kkzd2VkZGDixIkIDw9XSZOWlobZs2dLD8rWq1cPgwcP1vi9VzXavK6V5a/cDkCjKaKVaftzU1llZ2fjs88+k/7AtLW1xc8//6zysHlJVNQ+lv11xW5rXeqvBbGMxpilpqbiypUruHHjBmJiYpCYmAh9fX2YmZnBwcEBzZs3R+fOnTnUoJxcvXpV+rWuffv22LFjR6Fps7OzMWLECOnXOwMDA/Tq1QuNGjWCQqHArVu34O/vL6Xv0KEDtmzZUuhDqeHh4fD09JQuKgsLC/Tr1w8ODg6IiorCyZMnpV8mrKyscODAgUJ/KdRmXrqqPNr68uXLKg9UdujQoUS35d96660CX7KrV6/Gxo0bpf3atWujT58+sLW1RXx8PPz8/FSG0bz//vv4+uuvdW7YQ1HK67oWRRFz5szB0aNHpdfq1auHHj16oEaNGoiOjsb58+dVZv2ZNWsWJk+erLZuSUlJGDJkiDScqVq1aujbty8aNmyIuLg4nDp1Suq4jYyMsHPnTrRq1UrTfyadUJ7f4fm+/PJLHDx4EEBeYLRgwYISvQdtf24qowMHDsDb21va79u3L1q3bq3x+f3794e9vb3KaxW1j63q/XVFbWtd669lD3TS0tKwfv167N27Fy9fviwyraGhIQYOHIjZs2ejRo0aclaLXlGSThIAXrx4gU8//VSlM3yVIAjw9PTEwoULi+0g79+/Dy8vL0RERBSapm7duli/fn2xi05pMy9dVB5tPXv27NdaudzHx0ftYoDbtm3DqlWrpF/E1DE2NsakSZMwderUKhXkAOV7XWdnZ+P777/H1q1bi3wOpHr16pg7dy6GDx9eZN2io6MxdepU3L17t9A0NWrUwOrVq6VfNauS8v4OB/JmYsqfjGDu3LmYMGFCCd5BHm1/biqbESNGICAgoNTn//LLL2qHF1XUPrYq99cVta11rb9+7VnXihISEoKpU6ciLCysyDF5+W9GoVDgt99+w4ULF7Bx40bOmFOB1ahRA7/88gtOnTqFw4cPIygoCImJiTA0NETdunXRvn17eHp6avzF1LRpUxw/fhz79+/H6dOn8ejRIyQlJcHMzAyNGzdG3759MXz4cI1W5dZmXqSdtlaee1+bxo0bh3feeQd79+7FpUuX8OTJE6SmpsLCwgIODg7o1q0bPDw8dOpXQDlp87o2MDDA559/jmHDhmHv3r24evUqIiIikJ6eDisrKzg5OaFHjx4YMmQIbGxsis3P3t4eBw4cwJEjR3DixAncu3cPiYmJMDY2RsOGDdGjRw+MHDmSQxM1pO3vcEB1zYtXf2nWlLY/N5WNXN+VFbWPrcr9dUVta13rr2W7oxMXF4dhw4bh2bNnEARBCnRq1aqFxo0bw8LCAjk5OUhKSkJwcLA0P3d+2po1a+LAgQOl/rIkIiIiIqKqS7ZAZ9KkSfDz85MCl0GDBuHjjz9WWdRI2b1797B161YcO3ZMOuett97Cpk2b5KgeERERERHpMFkCnYCAAIwcORKCIEAQBCxfvhweHh4anXv48GF88cUXyM3NhSAI2Lt3L1q2bKntKhIRERERkQ6TZXrp48ePS9vjxo3TOMgBgMGDB2P8+PHS/pEjR7RaNyIiIiIi0n2yBDrXrl0DAOjr62PSpEklPn/SpEkwMMibJ+H69etarRsREREREek+WQKd58+fQxAEuLi4wNLSssTnW1hYwMXFBaIoFjk9HhERERERkTqyBDr5U0xaWFiUOo/q1asDQJHzbRMREREREakjS6CTfxdHefXikso/tzR3hIiIiIiIqGqTJdCpX78+RFFEaGgowsLCSnx+aGgoQkNDIQgC6tatK0MNiYiIiIhIl8kS6HTo0EHaXrlyZYnPX7FihbTduXNnrdSJiIiIiIiqDlkCnWHDhkmzpp07dw5LlixBdnZ2sedlZ2djyZIlOH/+PIC8WdtKMjU1ERERERERIFOgU6dOHYwcORL5a5Hu2bMHgwYNwu7du9UOZQsNDcWuXbswaNAg7NmzBwAgCAI8PT3h5OQkRxWJiIiIiEiHCWJ+NKJlCoUCH374If755x8IggBRFCEIAgDAwMBAmlUtJSVFutuTn0YURbRs2RI7d+6EkZGRHNUjIiIiIiIdJssdHQAwMjLC9u3b0b17dyjHUqIoIisrCy9evMCLFy+QlZVV4HjPnj2xdetWBjlERERERFQqst3RUebn54eNGzfin3/+QU5Ojto0enp6aNOmDcaPH4/evXvLXSUiIiIiItJhZRLo5EtNTcXNmzcRExODxMRE5ObmwsrKCnZ2dmjTpo00nI2IiMrP06dP0atXr2LTCYIAAwMDmJmZwcbGBvXr10e3bt3Qr18/roGmJfPnz4evry8AYMiQIfjmm2/KuUZERJWHQVkWZm5ujrfeeqssiyQiIpnkD0VOTExEYmIiHj16hLNnz2LJkiUYP348pk+fziHIRERUbso00CEiosrHxcUFtWvXLvB6bm4uMjIy8OLFC0RGRiIrKwsAkJWVhU2bNuH8+fPYsmUL7O3ty7rKREREDHSIiKho48ePx9ChQ4tMo1AocPnyZaxfvx7//PMPAODRo0cYM2YM9u/fDysrq7KoKhERkUS2WdeIiKjqMDIyQrdu3bBnzx588MEH0uvh4eGYP39+OdaMiIiqKgY6RESkNfr6+li4cKHK7Jl//PEH/Pz8yrFWRERUFTHQISIirRIEAUuWLIGZmZn02qZNm8qxRkREVBXxGR0iItK6mjVrYvjw4fj5558BANevX0dERAQcHR0LPSc4OBhHjhzB33//jejoaKSkpMDS0hKOjo5wd3fHe++9V+KJDfz9/XH06FHcvHlTmjDB0tISLi4u6NatG4YOHQoLC4ti84mKisLvv/+Oa9eu4fHjx0hKSkJ6ejrMzMxgbW2NVq1aSVNr6+vra1S3wMBA7N+/H9euXcOzZ89gYGCAunXrok+fPhg1ahRq1KhRovcKAGlpaTh06BD8/PwQHByM+Ph4GBkZoVatWmjbti3eeecddO7cucT5EhFVRmW6jg4REVV8r66j4+PjU+xkBOr8888/GD58uLS/cOFCjB49ukC69PR0LFu2DL/99htyc3MLzc/Y2BgTJ06El5cXBEEosuzw8HB4e3vj2rVrRaaztrbGsmXLCl2oOj09HT4+Pvjtt9+kWeWK0rBhQ6xbtw7Ozs6Fpnn58iWWLFkirY9TWL1WrFiBEydOaLyOzpEjR/DNN98gPj6+yDp27NgR//3vf2FnZ1fMuyEiqtw4dI2IiGTRokULmJqaSvsBAQEF0iQkJGD06NE4cOCAFOTo6emhSZMm6NixI1xcXKCnl9dVZWZmYt26dZgxYwZycnIKLff+/fvw9PRUCXIMDQ3h6uqKdu3aoVatWirlT58+HSdPniyQT3p6OsaOHYu9e/dKQY6RkRGaNm2KTp06oW3btrC1tVU5JyQkBGPHjkVKSoraumVmZmLixIkqQY6enh6aNWuG9u3bS8FHQkICpk2bhqCgoELfp7J169Zhzpw5KkGOra0t2rdvj9atW6ssyH3lyhV4enri8ePHGuVNRFRZcegaERHJQk9PD46Ojnjw4AEA4MmTJyrHRVHE3LlzpT/mBUHAhx9+iMmTJ6NmzZpSutjYWKxduxZ79+4FAJw6dQpr1qzBrFmzCpT58uVLTJs2DYmJiQDyJkeYPHkyxo8fr/LH/tmzZ/H1118jJiYGubm58Pb2xptvvqkSBP3444/SVNmCIMDLywvjx49XefYIyAusli1bBn9/f6m++/btw4QJEwrUb9WqVbh+/bq0369fP3h7e6sETBcvXsSiRYsQGRmJ4ODgQv998x0/fhxr166V9t944w14e3vDzc1Nei0rKwvHjx+Hj48PEhMTERMTAy8vLxw4cKDA+yEi0hW8o0NERLJRfqYmOjpa5diRI0dw4cIFaX/p0qXw9vZWCXIAoFatWliyZAlmz54tvbZp0yaEhoYWKG/btm14+vSptP/tt9/i008/VQlyAKB379743//+h2rVqgEAUlNTsXPnTul4eno6duzYIe1PmDABXl5eaoOCpk2bYsuWLQWClVdFRERg165d0v6gQYPwww8/FLgr5O7ujj179mg0tCw1NRVfffWVtN+xY0fs2bNHJcgB8u5oeXh4YPfu3bC0tASQd/dp8+bNxZZBRFRZyRLoHDp0CIcOHcLly5cLTXP58mUpHRER6SYTExNpOy0tTeXY1q1bpe2+ffvC09OzyLw++eQTtGjRAgCQm5uLbdu2qRzPzc2V7voAwLvvvov+/fsXmp+rqysGDhwo7Z8+fVravnHjBhQKBYC8IGHixIlF1q1atWro1q2btP/s2bMCaY4dO4bs7GwAgKWlJRYtWlTos0a2trZYuHBhkWUCwIEDB5CcnAwAMDU1xcqVK2FkZFRoemdnZ8yYMUPa37NnDzIyMooth4ioMpJl6Nr8+fMhCALc3d3RqVMntWm2bt2Kv//+G4IgwMPDQ45qEBFROTMw+LebUX6uJiwsTBrSBgCjRo3SKL9Bgwbh9u3bAFBgbZ67d++qBBgjR44sNr8xY8agfv36aNCgARo0aCC97u7ujsDAQERGRiIxMRFWVlbF5mVjYyNtZ2ZmFjj+559/Sttvv/12gbtMr+rZsyfs7e0L3AlTdurUKWm7T58+qF27drH1HDBgAP7zn/8gJycHiYmJuHXrFjp27FjseURElU2pA51PP/0Ubdq0QZs2beDq6gpDQ8MS58EJ34iIdFtqaqq0bW5uLm3fvHlTJV2rVq00ys/V1VXajoqKwrNnz6QhXoGBgdIxQ0NDjfJ0cXGBi4uL2mMGBgaoV68e6tWrV2QesbGxCAgIwJUrV6TXXp0sQaFQqNTv1aFl6ujp6aF9+/Y4fPiw2uMKhUJlsgJN/w0tLS1Rt25dhIWFAchrCwY6RKSLSh3onD59GmfOnAGQNwuNq6urFPgQEREBUJl9TPkORv4f2flK23c8f/5cCnQiIiKk1x0cHIocwlVSubm5ePLkCYKDgxEREYGIiAiEh4fj0aNHiI2NLZD+1R/y4uLiVKbOrl+/vkblNmrUqNBjz549k4bXAcCSJUuwZMkSjfJV9vz58xKfQ0RUGbzW0LX8L/LMzEwEBAQUmDr04cOH2LBhA1q0aIEWLVpID0ASEVHVEBMTI20rD6vKf67kdeXPrgao3j0qbliYpl68eIHNmzfjyJEjiIuLKzKtgYGB9AzOq15d20b57lZRilrMNCkpSaM8iqOtfIiIKppSBzqXLl3CzZs3pQAnKChI5ZclURQRExODNWvWSK85OTnhjTfeQMuWLfHixYvXqzkREVVoiYmJCA8Pl/bzJxIAoBIQmJubo3Xr1qUqQzkQKG4R0ZK6ceMGpk6dqhJMKZdlb2+PJk2aoHXr1ujUqRP8/Pywfv16tXlpstioOkXdlXo1qHJzc1NZt0hTRd01IiKqzEod6NSoUQO9e/eWVpPOysrCnTt3cPPmTaxYsQKCIBS4dR8WFobw8HCcOHFC5fUvvvgCrq6ucHV1RbNmzaTpPomIqPLKX4Mmn/IzJMoBiqWlpcoMbKWlnGdhC3Zq6tmzZ5gyZYp0t0NPTw99+vRB165d0bx5czRs2FBlRjkAOH/+vEZ1AzS/i+Xuh/oAAAkSSURBVKJ8l6q4PGfOnIkOHTpolC8RUVWgtVnXDA0N0bp1a7Ru3RorVqwAALRr1w6TJ09GYGAgAgMDcfv27QK3/kVRVJlmWk9PDw0bNpQCn+bNm+PNN9/UVjWJiKiMKD9EX61aNXTt2lXaV1475tmzZ0hJSXnt4WbKeUZGRiIrK0ujiXL27dsHOzs7ODo6om7dujA0NMTGjRulYMTQ0BCbN28udBbRfEUFL46OjtDT05Oe0wkJCdGob1O+I/YqW1tblR8VHz16xECHiEiJLNNL56tWrRq6dOmCLl26SK89e/YMgYGB+O677/DkyZMCd35ycnLw6NGj/7d3dyFN/X8cwN/HzKfsl1uNqcsuhMy2FMogSMMycVKQBkoPaiwwiSISQ6mk9KqMvEqvUkm7yNDAhJIiwXDqiLBM2XxAXOYyo1RQESXX/hfi+e/4sPUz7WG/9wuEnbOz747eyPt8v9/PB729vaitrYUgCDCZTKt5m0REtMI+f/4s6UsTGxsrCTL2xQesViv0er3Dnjdz3rx5g5qaGqhUKqhUKsTExIhNPO1njL59+4aOjg6n1c0GBgYk/Wpqa2sRGhoqKV0dFxfnNOQAQFdXl/javvAAAHh6ekKtVotV0lpaWpz2DQJml88txdfXF1u3bkVPTw8AoKGhASkpKU7HtFqtyM/Ph0wmg0qlQkREBJevEZFLWtWgsxh/f3/4+/ujqqoKZrMZAPDixQuYTCYYjUYYjUaYTCaMjo7+6lsjIqIVYLPZkJubK+5LcXNzW9Bwc/v27VAqlWLFr9LSUsTHx8PNzXEf6+LiYjQ3NwMA/Pz8EB8fL76nVquxadMmceVAdXW106Dz9OlT8bVcLhdLTdsXUVAoFA7HAGaX6bW1tYnH88tLA4BWqxWDTn19PQYHBxEYGLjkmO3t7U4f9O3fv18MOk1NTTCZTFCr1Q4/U1dXh6qqKvH4zp07DDpE5JIc/0dxYH5p0J8RFBQErVaLrKwslJWVwWAwoKGhAcXFxTh79uyKfQ8REa0uq9WKmzdvQq/Xi+eOHTuG0NBQyXVr1qyBTqcTj41Go7jseSnV1dViyAGApKQkydI0d3d3SZPQmpoaNDY2Ljnex48fJXuDkpKSxKBl3yDUYDAsWU0NAD59+oTs7GzJ6oTFig8kJydDJpMBmO2Bk5OTs2hjUWB2b05ubu6S3zknNTVV/BvYbDZkZWU5LPYzNDSEgoIC8TggIAAHDhxw+j1ERH+jZQcdrVaLvXv34ty5cygtLUVra6uk6trPCggIQGxsLC5evLhiYxIR0eqYnJxEXV0dkpOTUVFRIZ4PDw/HlStXFv1MamqqpAHovXv3cOHCBbx//15y3ejoKAoLC3H9+nXxnFKpREZGxoIxdTqd2KPGZrPh/PnzKC8vx9TUlOS6lpYWpKWliWWuFQoF0tPTxfft9xN1d3fj8uXLC0pEj4+P48GDB0hMTFzw8G9ycnLBvclkMly9elU8fv36NU6dOoXu7m7Jde3t7Th58qQ4U+OIUqlEZmameGw2m5GUlIT6+nrJ8jmbzYa6ujqcOHFCslc2JydnRfsNERH9SQTb/NJoPyg0NHRBKU93d3eo1Wq8e/cOgiBgz549KC8vX/Tz6enpaGpqgiAI6OzsXM4tEBHRKrBYLDh48KB4HBISIumBM8dms2Fqagpfv36FxWJZsFwrIiICxcXFkMvlDr9Lp9NJmn0Csw01lUolhoeH8eHDB8mDtPXr16OsrEyyJ8ded3c3Tp8+LQkmPj4+CAkJgaenJ/r7+zE0NCS+5+3tjdLSUuzevVs819/fj8TERElg8fDwwJYtWyCXyzE8PIyBgQHJfQUGBmJwcFA8NhgMi/7uN27ckIRBAAgODoZCocDQ0JAYmtzc3BATE4P6+noAwNGjRyWzMfZyc3Px6NEjyTk/Pz8EBwfDarWiv79/QZnsjIwMXLp0adHxiIhcwbKDzrVr19DW1obe3l7JdP1c+LHZbBAEAT4+PlCr1QgLC0N4eDjCwsKgUqkYdIiI/lDzg86/9c8//+DMmTPQ6XQ/NFswOjqKvLw8PH/+3Om1Go0GBQUF4l6apVgsFuTk5DjczA/MBqrCwkJJj585BoMBmZmZi/bRsefu7o60tDRkZGQgMjJSnEkpKipCXFzcop+pqKjA7du3l+yv4+Xlhby8PExPTyM/Px+A46ADAPfv30dRUZHTZqy+vr7Izs7G8ePHHV5HRPS3W3bQmTMxMYG3b9+KzUPb29sxOTkpqaY2f+bHz88PMzMzGB8fhyAIePnypaQsKBER/T4/GnQEQYC3tzfWrVuHzZs3IyQkBJGRkYiOjl5WP7TOzk48efIEr169wuDgIMbGxrB27VoolUqEhYXh0KFDiI6OdlqwwJ5er8ezZ8/Q2tqKL1++YHp6GjKZDBqNBlqtFocPH3YYxkZGRvDw4UM0NjbCbDZjYmICHh4e4mzJzp07kZCQgKCgIACzS+cMBgMAIDo6Gnfv3l1ybIvFgsrKSuj1erGMtFKpRFRUFFJSUhAcHIzKysofDjoAMDY2hsePH6O5uRk9PT0YGRnB9+/fsWHDBmzbtg379u1DQkKCuFeIiMiV/XTQmc9qtUKj0UAQBGzcuBGBgYHo6upasH9n/syPXC4Xe+doNBrs2LED/v7+K3lrRERERET0H7HiQQf4//6dqKgolJSUYGZmBl1dXejo6BB/+vr6xOn9pWZ+5p66lZSUrPQtEhERERGRC/slQWcx9nt0jhw5AqPRCLPZvGAzK/fwEBERERHRv/XLG4Yu5tatWwCAqakpSeNQo9GIvr6+33x3RERERET0t/kjgs4cLy8v7Nq1S9LJen7vAyIiIiIiImdWJegkJiZCEASH5T8jIyOhUCicjrWcyj1ERERERPTftip7dIiIiIiIiH6nH29GQERERERE9Jdg0CEiIiIiIpfDoENERERERC6HQYeIiIiIiFwOgw4REREREbkcBh0iIiIiInI5DDpERERERORyGHSIiIiIiMjlMOgQEREREZHLYdAhIiIiIiKXw6BDREREREQuh0GHiIiIiIhcDoMOERERERG5HAYdIiIiIiJyOQw6RERERETkcv4HqcmSjqj7l7sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq_data = np.array([\n",
    "    0, # 1900-1909\n",
    "    1, # 1910-1919\n",
    "    2, # 1920-1929\n",
    "    0, # 1930-1939\n",
    "    3, # 1940-1949\n",
    "    2, # 1950-1959\n",
    "    1, # 1960-1969\n",
    "    2, # 1970-1979\n",
    "    1, # 1980-1989\n",
    "    4, # 1990-1999\n",
    "    0, # 2000-2009\n",
    "    2 # 2010-2019 \n",
    "])\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "ax.bar(np.linspace(1900, 2019, eq_data.shape[0]), eq_data, width=10)\n",
    "ax.set_xlabel('Decade')\n",
    "ax.set_ylabel('# of major earthquakes in Southern CA');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We model the number of earthquakes $y_i$ in a decade $i$ is using a Poisson distribution with unknown rate parameter $\\lambda$, i.e.,\n",
    "$$\n",
    "y_i | \\lambda \\sim \\operatorname{Poisson}(\\lambda).\n",
    "$$\n",
    "The rate parameter $\\lambda$ (number of major earthquakes in per ten years) is positive.\n",
    "A suitable choice here is to pick a [Gamma](https://en.wikipedia.org/wiki/Gamma_distribution), see also [the scipy.stats page for the Gamma](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gamma.html).\n",
    "We write:\n",
    "$$\n",
    "\\lambda \\sim \\operatorname{Gamma}(\\alpha, \\beta),\n",
    "$$\n",
    "where $\\alpha$ and $\\beta$ are positive *hyper-parameters* that we have to set to represent our prior state of knowledge.\n",
    "We pick $\\alpha$ and $\\beta$ by looking at the mean and the variance of the Gamma.\n",
    "I expect the rate $\\lambda$ to be about 2 major earthquakes per decade. So I would set for the expectation:\n",
    "$$\n",
    "\\mathbb{E}[\\lambda] = \\frac{\\alpha}{\\beta} = 2.\n",
    "$$\n",
    "On the other hand, I am not so sure about it.\n",
    "To model this, I would pick a large variance for $\\lambda$.\n",
    "I would pick a variance of $4$:\n",
    "$$\n",
    "\\mathbb{V}[\\lambda] = \\frac{\\alpha}{\\beta^2} = 4.\n",
    "$$\n",
    "Solving the second equation for $\\alpha$ I get:\n",
    "$$\n",
    "\\alpha = 4\\beta^2.\n",
    "$$\n",
    "Plugging this in the first equation I get:\n",
    "$$\n",
    "\\frac{4\\beta^2}{\\beta} = 2,\n",
    "$$\n",
    "and solving for $\\beta$ I get:\n",
    "$$\n",
    "\\beta = 0.5.\n",
    "$$\n",
    "Therefore, my $\\alpha$ is:\n",
    "$$\n",
    "\\alpha = 1.\n",
    "$$\n",
    "Let's plot this to see if it makes sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 1.0  # Pick them here\n",
    "beta = 0.5  # Pick them here\n",
    "lambda_prior = st.gamma(alpha, scale=1.0 / beta) # Make sure you understand why scale = 1 / beta\n",
    "lambdas = np.linspace(0, lambda_prior.ppf(0.99), 100)\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "ax.plot(lambdas, lambda_prior.pdf(lambdas))\n",
    "ax.set_xlabel('$\\lambda$ (# or major earthquakes per decade)')\n",
    "ax.set_ylabel('$p(\\lambda)$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks fine. \n",
    "Now, as you showed in the homework, the posterior for $\\lambda$ conditioned on the data is also a Gamma:\n",
    "$$\n",
    "\\lambda | y_{1:n} \\sim \\operatorname{Gamma}(\\alpha+\\sum_{i=1}^ny_i, \\beta + n).\n",
    "$$\n",
    "Let's visualize it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambda_post = st.gamma(alpha + eq_data.sum(), scale=1.0 / (beta + eq_data.shape[0])) # Make sure you understand why scale = 1 / beta\n",
    "lambdas = np.linspace(0, lambda_prior.ppf(0.99), 100)\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "ax.plot(lambdas, lambda_prior.pdf(lambdas), label='Prior')\n",
    "ax.plot(lambdas, lambda_post.pdf(lambdas), '--', label='Posterior')\n",
    "ax.set_xlabel('$\\lambda$ (# or major earthquakes per decade)')\n",
    "ax.set_ylabel('$p(\\lambda)$')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now draw replicated data from our model and compare them to the original data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_rep = 9\n",
    "y_rep = np.ndarray((n_rep, eq_data.shape[0]))\n",
    "for i in range(n_rep):\n",
    "    lambda_post_sample = lambda_post.rvs()\n",
    "    y_rep[i, :] = st.poisson(lambda_post_sample).rvs(size=eq_data.shape[0])\n",
    "fig, ax = plt.subplots(5, 2, sharex='all', sharey='all', figsize=(20, 20))\n",
    "ax[0, 0].bar(np.linspace(1900, 2019, eq_data.shape[0]), eq_data, width=10, color='red')\n",
    "for i in range(1, n_rep + 1):\n",
    "    ax[int(i / 2), i % 2].bar(np.linspace(1900, 2019, eq_data.shape[0]), y_rep[i-1], width=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually, it is not apparent that there is any problem with the model.\n",
    "We don't have enough data to see any paterns anyway.\n",
    "What are some good test quantities to check?\n",
    "Here are some ideas:\n",
    "\n",
    "+ Maximum number of consecutive decades with no earthquakes.\n",
    "+ Maximum number of consecutive decades with earthquakes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The observed test quantity is 1\n",
      "The Bayesian p_value is 0.4012\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def T_eq_max_neq(y):\n",
    "    \"\"\"\n",
    "    Return the maximum number of consecutive decades with no earthquakes.\n",
    "    \"\"\"\n",
    "    count = 0\n",
    "    result = 0\n",
    "    for i in range(y.shape[0]):\n",
    "        if y[i] != 0:\n",
    "            count = 0\n",
    "        else:\n",
    "            count += 1\n",
    "            result = max(result, count)\n",
    "    return result\n",
    "    \n",
    "# The observed test quantity\n",
    "T_eq_max_neq_obs = T_eq_max_neq(eq_data)\n",
    "print('The observed test quantity is {0:d}'.format(T_eq_max_neq_obs))\n",
    "# Draw replicated data\n",
    "n_rep = 5000\n",
    "y_rep = np.ndarray((n_rep, eq_data.shape[0]))\n",
    "for i in range(n_rep):\n",
    "    lambda_post_sample = lambda_post.rvs()\n",
    "    y_rep[i, :] = st.poisson(lambda_post_sample).rvs(size=eq_data.shape[0])\n",
    "# Evaluate the test quantity\n",
    "T_eq_max_neq_rep = np.ndarray(y_rep.shape[0])\n",
    "for i in range(y_rep.shape[0]):\n",
    "    T_eq_max_neq_rep[i] = T_eq_max_neq(y_rep[i, :])\n",
    "# Estimate the Bayesian p-value\n",
    "p_val = np.sum(np.ones((n_rep,))[T_eq_max_neq_rep > T_eq_max_neq_obs]) / n_rep\n",
    "print('The Bayesian p_value is {0:1.4f}'.format(p_val))\n",
    "# Do the plot\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "tmp = ax.hist(T_eq_max_neq_rep, density=True, alpha=0.25, label='Replicated test quantity')[0]\n",
    "ax.plot(T_eq_max_neq_obs * np.ones((50,)), np.linspace(0, tmp.max(), 50), 'k', label='Observed test quantity')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And it looks reasonable.\n",
    "It is very possible that we have a pretty good model for this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions\n",
    "+ Modify the code above to check what happens for the test statisstic \"maximum number of consecutive decades with at least one earthquake.\""
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
